diff --git a/.gitignore b/.gitignore index d9005f2..7c0ae4b 100644 --- a/.gitignore +++ b/.gitignore @@ -150,3 +150,9 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +# Differential Gaussian Rasterization +diff-gaussian-rasterization/ + +# Nvdiffrast +nvdiffrast/ \ No newline at end of file diff --git a/README.md b/README.md index 48d9d66..44475fc 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,83 @@ # ComfyUI-3D-Pack - An extensive node suite that enables ComfyUI to process 3D inputs (Mesh & UV Texture, etc) using cutting edge algorithms (3DGS, NeRF, etc.) + An extensive node suite that enables ComfyUI to process 3D inputs (Mesh & UV Texture, etc) using cutting edge algorithms (3DGS, NeRF, Differentiable Rendering, SDS/VSD Optimization, etc.) + +### Note: this project is still a WIP and not been released into ComFyUI package database yet + +ps: I'll show some generated result soon + +--- +
+ +**[IMPORTANT!!!]**
Currently this package is only been tested in following setups: +- Windows 10/11 +- Miniconda/Conda Python 3.11.7 + - I tried install this package with ComfyUI embed python env first, but I can't find a way to build CUDA related libraries, e.g. diff-gaussian-rasterization, nvdiffrast, simple-knn. +- Torch version: 2.1.2+cu121/V.2.1.2+cu118 + +### Install: + +Assume you have already downloaded [ComfyUI](https://github.com/comfyanonymous/ComfyUI) + +First download [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/) (*One of the best way to manage a clean and separated python envirments*) +- Alternatively you can check this tutorial: [Installing ComfyUI with Miniconda On Windows and Mac](https://www.comflowy.com/preparation-for-study/install#step-two-download-comfyui) + +```bash +# Go to your Your ComfyUI root directory, for my example: +cd C:\Users\reall\Softwares\ComfyUI_windows_portable + +conda create -p ./python_miniconda_env/ComfyUI python=3.11 + +# conda will tell what command to use to activate the env +conda activate C:\Users\reall\Softwares\ComfyUI_windows_portable\python_miniconda_env\ComfyUI + +# This package also works with cu118 +pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 + +pip install -r ./ComfyUI/requirements.txt + +# Then go to ComfyUI-3D-Pack directory under the .\ComfyUI\custom_nodes for my example is: +cd C:\Users\reall\Softwares\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-3D-Pack +# Finally you can double click following .bat script or run it in CLI: +install.bat +``` + +Just in case `install.bat` may not working in your PC, you could also run the following commands under this package's root directory: +```bash +# First make sure the Conda env: python_miniconda_env\ComfyUI is activated, then go to Go to ComfyUI Root Directory\ComfyUI\custom_nodes\ComfyUI-3D-Pack and: +pip install -r requirements.txt + +git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization +pip install ./diff-gaussian-rasterization + +pip install ./simple-knn + +git clone --recursive https://github.com/NVlabs/nvdiffrast/` +pip install ./nvdiffrast +``` + +### Run: +Copy the files inside folder [__New_ComfyUI_Bats](./_New_ComfyUI_Bats/) to your ComfyUI root directory, and double click run_nvidia_gpu_miniconda.bat to start ComfyUI! +- Alternatively you can just activate the Conda env: python_miniconda_env\ComfyUI, and go to your ComfyUI root directory then run command `python ./ComfyUI/main.py` + +--- + +### Currently support: +- For use case please check [example workflows](./_Example_Workflows/) + - **Note:** you need to put [Images_enhanced](_Example_Workflows/_Example_Inputs_Files/Images_enhanced) and [Meshes](./_Example_Workflows/_Example_Inputs_Files/Meshes) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow +- Load 3D file (.obj, .ply, .glb) +- 3D Gaussian Splatting, with: + - [Improved Differential Gaussian Rasterization](https://github.com/ashawkey/diff-gaussian-rasterization) + - Better Compactness-based Densification method from [Gsgen](https://gsgen3d.github.io/), + - Support initialize gaussians from given 3D mesh (Optional) + - Support mini-batch optimazation + - Multi-View images as inputs + - Export to .ply support + +- Bake Multi-View images into UVTexture of given 3D mesh using [Nvdiffrast](https://github.com/NVlabs/nvdiffrast), supports: + - Export to .obj, .ply, .glb + +### To-Do Next: +- Add interactive 3D UI inside ComfuUI to visulaize training and generated results for 3D representations +- Add DMTet algorithm to allow convertion from points cloud(Gaussian/.ply) to mesh (.obj, .ply, .glb) +- Add a general SDS/VSD Optimization algorithm to allow training 3D representations with diffusion model, *The real fun begins here* ;) +- Add a few best Nerf algorithms (No idea yet, [instant-ngp](https://github.com/NVlabs/instant-ngp) maybe?) \ No newline at end of file diff --git a/_Example_Workflows/3DGS_Multi-View_Renders_to_ply.json b/_Example_Workflows/3DGS_Multi-View_Renders_to_ply.json new file mode 100644 index 0000000..400b719 --- /dev/null +++ b/_Example_Workflows/3DGS_Multi-View_Renders_to_ply.json @@ -0,0 +1,455 @@ +{ + "last_node_id": 19, + "last_link_id": 22, + "nodes": [ + { + "id": 10, + "type": "PreviewImage", + "pos": [ + 1410, + -320 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 4 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 9, + "type": "MaskToImage", + "pos": [ + 1110, + -320 + ], + "size": { + "0": 210, + "1": 26 + }, + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": "mask", + "type": "MASK", + "link": 8 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 4 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "MaskToImage" + } + }, + { + "id": 17, + "type": "[ComfyUI-3D] Save 3DGS", + "pos": [ + 2122, + 54 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [ + { + "name": "raw_3DGS", + "type": "GS_RAW", + "link": 21 + } + ], + "properties": { + "Node name for S&R": "[ComfyUI-3D] Save 3DGS" + }, + "widgets_values": [ + "MeshTest\\3DGS_01.ply" + ] + }, + { + "id": 8, + "type": "InvertMask", + "pos": [ + 746, + -14 + ], + "size": { + "0": 210, + "1": 26 + }, + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "name": "mask", + "type": "MASK", + "link": 2 + } + ], + "outputs": [ + { + "name": "MASK", + "type": "MASK", + "links": [ + 8, + 18 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "InvertMask" + } + }, + { + "id": 4, + "type": "[ComfyUI-3D] Generate Orbit Camera Poses", + "pos": [ + 830, + 132 + ], + "size": { + "0": 400, + "1": 200 + }, + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [ + { + "name": "reference_images", + "type": "IMAGE", + "link": 1 + } + ], + "outputs": [ + { + "name": "orbit_camposes", + "type": "ORBIT_CAMPOSES", + "links": [ + 19 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[ComfyUI-3D] Generate Orbit Camera Poses" + }, + "widgets_values": [ + "([0:120], 1.75, 0, 0, 360)" + ] + }, + { + "id": 18, + "type": "[ComfyUI-3D] Gaussian Splatting", + "pos": [ + 1486, + 53 + ], + "size": { + "0": 468.5999755859375, + "1": 718 + }, + "flags": { + "collapsed": false + }, + "order": 6, + "mode": 0, + "inputs": [ + { + "name": "reference_images", + "type": "IMAGE", + "link": 17 + }, + { + "name": "reference_masks", + "type": "MASK", + "link": 18 + }, + { + "name": "reference_orbit_camera_poses", + "type": "ORBIT_CAMPOSES", + "link": 19 + }, + { + "name": "mesh_to_initialize_gaussian", + "type": "MESH", + "link": null + } + ], + "outputs": [ + { + "name": "raw_3DGS", + "type": "GS_RAW", + "links": [ + 21 + ], + 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a/_Example_Workflows/_Example_Inputs_Files/Meshes/Cammy_Uniform_albedo.png b/_Example_Workflows/_Example_Inputs_Files/Meshes/Cammy_Uniform_albedo.png new file mode 100644 index 0000000..54d04c7 Binary files /dev/null and b/_Example_Workflows/_Example_Inputs_Files/Meshes/Cammy_Uniform_albedo.png differ diff --git a/_New_ComfyUI_Bats/run_nvidia_gpu_miniconda.bat b/_New_ComfyUI_Bats/run_nvidia_gpu_miniconda.bat new file mode 100644 index 0000000..449a809 --- /dev/null +++ b/_New_ComfyUI_Bats/run_nvidia_gpu_miniconda.bat @@ -0,0 +1,2 @@ +.\python_miniconda_env\ComfyUI\python.exe -s ComfyUI\main.py --windows-standalone-build +pause diff --git a/_New_ComfyUI_Bats/update/update_comfyui_and_python_dependencies_miniconda.bat b/_New_ComfyUI_Bats/update/update_comfyui_and_python_dependencies_miniconda.bat new file mode 100644 index 0000000..cbee4da --- /dev/null +++ b/_New_ComfyUI_Bats/update/update_comfyui_and_python_dependencies_miniconda.bat @@ -0,0 +1,11 @@ +@echo off +..\python_miniconda_env\ComfyUI\python.exe .\update.py ..\ComfyUI\ +echo - +echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff +echo You should not be running this anyways unless you really have to +echo - +echo If you just want to update normally, close this and run update_comfyui.bat instead. +echo - +pause +..\python_miniconda_env\ComfyUI\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121 -r ../ComfyUI/requirements.txt pygit2 +pause diff --git a/diff_rast/diff_mesh_renderer.py b/diff_rast/diff_mesh_renderer.py index 1267727..90a14a5 100644 --- a/diff_rast/diff_mesh_renderer.py +++ b/diff_rast/diff_mesh_renderer.py @@ -66,10 +66,6 @@ class Renderer(nn.Module): return params - def export_mesh(self, save_path): - self.mesh.write(save_path) - - @torch.no_grad() def update_mesh(self): self.mesh.v = (self.mesh.v + self.v_offsets).detach() self.mesh.albedo = torch.sigmoid(self.raw_albedo.detach()) @@ -106,7 +102,6 @@ class Renderer(nn.Module): texc, texc_db = dr.interpolate(self.mesh.vt.unsqueeze(0).contiguous(), rast, self.mesh.ft, rast_db=rast_db, diff_attrs='all') albedo = dr.texture(self.raw_albedo.unsqueeze(0), texc, uv_da=texc_db, filter_mode=texture_filter) # [1, H, W, 3] - print(f"albedo = dr.texture: {albedo.requires_grad}") albedo = torch.sigmoid(albedo) # get vn and render normal if self.train_geo: @@ -134,7 +129,6 @@ class Renderer(nn.Module): # antialias albedo = dr.antialias(albedo, rast, v_clip, self.mesh.f).squeeze(0) # [H, W, 3] - print(f"albedo = dr.antialias: {albedo.requires_grad}") albedo = alpha * albedo + (1 - alpha) * bg_color # ssaa diff --git a/diff_rast/diff_texturing.py b/diff_rast/diff_texturing.py index 8c1e527..2dff5e3 100644 --- a/diff_rast/diff_texturing.py +++ b/diff_rast/diff_texturing.py @@ -1,4 +1,5 @@ import random +import tqdm import torch import torch.nn.functional as F @@ -8,7 +9,7 @@ from pytorch_msssim import SSIM, MS_SSIM from ..diff_rast.diff_mesh_renderer import Renderer from ..shared_utils.camera_utils import orbit_camera, OrbitCamera -class DiffTextureBaker: +class DiffTextureBaker: def __init__(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, mesh, training_iterations, batch_size, texture_learning_rate, train_mesh_geometry, geometry_learning_rate, ms_ssim_loss_weight, force_cuda_rasterize): @@ -62,7 +63,7 @@ class DiffTextureBaker: ref_imgs_num_minus_1 = self.ref_imgs_num-1 - for step in range(self.training_iterations): + for step in tqdm.trange(self.training_iterations): ### calculate loss between reference and rendered image from known view loss = 0 @@ -83,13 +84,8 @@ class DiffTextureBaker: image = out["image"] # [H, W, 3] in [0, 1] image = image.permute(2, 0, 1).contiguous() # [3, H, W] in [0, 1] - #print(f"image.requires_grad: {image.requires_grad}") - image_masked = (image * self.ref_masks_torch[i]).unsqueeze(0) - #print(f"image_masked.requires_grad: {image_masked.requires_grad}") - #print(f"ref_imgs_masked[i].requires_grad: {ref_imgs_masked[i].requires_grad}") - masked_rendered_img_batch.append(image_masked) masked_ref_img_batch.append(ref_imgs_masked[i]) @@ -103,33 +99,22 @@ class DiffTextureBaker: # [1, 3, H, W] in [0, 1] #loss += self.lambda_ssim * (1 - self.ssim_loss(X, Y)) loss += self.lambda_ssim * (1 - self.ms_ssim_loss(masked_ref_img_batch_torch, masked_rendered_img_batch_torch)) - - print(f"masked_rendered_img_batch_torch.requires_grad: {masked_rendered_img_batch_torch.requires_grad}") - print(f"masked_ref_img_batch_torch.requires_grad: {masked_ref_img_batch_torch.requires_grad}") - - print(f"loss.requires_grad: {loss.requires_grad}") - - print(f"self.renderer.raw_albedo.requires_grad: {self.renderer.raw_albedo.requires_grad}") - - # import kiui - # kiui.lo(hor, ver) - # kiui.vis.plot_image(image) # optimize step loss.backward() self.optimizer.step() self.optimizer.zero_grad() - torch.cuda.synchronize() + torch.cuda.synchronize() - self.need_update = True + self.need_update = True - print(f"Step: {step}") + print(f"Step: {step}") self.renderer.update_mesh() ender.record() - t = starter.elapsed_time(ender) + #t = starter.elapsed_time(ender) def get_mesh_and_texture(self): return (self.renderer.mesh, self.renderer.mesh.albedo, ) \ No newline at end of file diff --git a/gaussian_splatting/main_3DGS.py b/gaussian_splatting/main_3DGS.py new file mode 100644 index 0000000..f3ee6d0 --- /dev/null +++ b/gaussian_splatting/main_3DGS.py @@ -0,0 +1,228 @@ +import random +import tqdm + +import torch +import torch.nn.functional as F +import numpy as np +from pytorch_msssim import SSIM, MS_SSIM + +from .main_3DGS_renderer import Renderer +from ..shared_utils.camera_utils import orbit_camera, OrbitCamera, MiniCam, calculate_fovX, get_projection_matrix + +class GSParams: + def __init__(self, + training_iterations=3000, + batch_size=1, + loss_scale=10000, + lambda_ssim=0.2, + lambda_alpha=3, + lambda_offset=0, + lambda_offset_opacity=0, + invert_bg_prob = 0.5, + feature_lr=0.01, + opacity_lr=0.05, + scaling_lr=0.005, + rotation_lr=0.005, + position_lr_init=0.001, + position_lr_final=0.00002, + position_lr_delay_mult=0.02, + position_lr_max_steps=500, + num_pts=5000, + K=3, + percent_dense=0.01, + density_start_iter=100, + density_end_iter=100000, + densification_interval=100, + opacity_reset_interval=700, + densify_grad_threshold=0.01, + sh_degree=0): + + # training params + self.training_iterations = training_iterations + self.batch_size = batch_size + self.loss_scale = loss_scale + self.lambda_ssim = lambda_ssim + self.lambda_alpha = lambda_alpha + self.lambda_offset = lambda_offset + self.lambda_offset_opacity = lambda_offset_opacity + self.invert_bg_prob = invert_bg_prob + + # learning params + self.feature_lr = feature_lr + self.opacity_lr = opacity_lr + self.scaling_lr = scaling_lr + self.rotation_lr = rotation_lr + self.position_lr_init = position_lr_init + self.position_lr_final = position_lr_final + self.position_lr_delay_mult = position_lr_delay_mult + self.position_lr_max_steps = position_lr_max_steps + + # densify and prune params + self.num_pts = num_pts + self.K = K + self.percent_dense = percent_dense + self.density_start_iter=density_start_iter + self.density_end_iter=density_end_iter + self.densification_interval=densification_interval + self.opacity_reset_interval=opacity_reset_interval + self.densify_grad_threshold=densify_grad_threshold + + # other gaussian params + self.sh_degree = sh_degree + + +class GaussianSplatting: + + def __init__(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, gs_params=None, init_input=None): + self.device = torch.device("cuda") + + self.ref_imgs_num = len(reference_images) + + self.all_ref_cam_poses = reference_orbit_camera_poses + self.ref_cam_fovy = reference_orbit_camera_fovy + + self.ref_size_H = reference_images[0].shape[0] + self.ref_size_W = reference_images[0].shape[1] + + # default camera settings + self.cam = OrbitCamera(self.ref_size_W, self.ref_size_H, fovy=reference_orbit_camera_fovy) + self.projection_matrix = get_projection_matrix(self.cam.near, self.cam.far, self.cam.fovx, self.cam.fovy).transpose(0, 1).cuda() + + # prepare renderer for optimization + if gs_params is None: + gs_params = GSParams() + + self.renderer = Renderer(sh_degree=gs_params.sh_degree) + self.gaussain_scale_factor = 1 + self.renderer.initialize(init_input, num_pts=gs_params.num_pts) + + # setup training + self.renderer.gaussians.training_setup(gs_params) + # do not do progressive sh-level + self.renderer.gaussians.active_sh_degree = self.renderer.gaussians.max_sh_degree + self.optimizer = self.renderer.gaussians.optimizer + #self.ssim_loss = SSIM(data_range=1, size_average=True, channel=3) + self.ms_ssim_loss = MS_SSIM(data_range=1, size_average=True, channel=3) + + self.gs_params = gs_params + + # prepare reference images and masks + ref_imgs_torch_list = [] + ref_masks_torch_list = [] + for i in range(self.ref_imgs_num): + ref_imgs_torch_list.append(self.prepare_img(reference_images[i])) + ref_masks_torch_list.append(self.prepare_img(reference_masks[i].unsqueeze(2))) + + self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0) + self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0) + + def prepare_img(self, img): + img_new = img.permute(2, 0, 1).unsqueeze(0).to(self.device) + img_new = F.interpolate(img_new, (self.ref_size_H, self.ref_size_W), mode="bilinear", align_corners=False).contiguous() + return img_new + + def training(self): + starter = torch.cuda.Event(enable_timing=True) + ender = torch.cuda.Event(enable_timing=True) + starter.record() + + ref_imgs_masked = [] + for i in range(self.ref_imgs_num): + ref_imgs_masked.append((self.ref_imgs_torch[i] * self.ref_masks_torch[i]).unsqueeze(0)) + + ref_imgs_num_minus_1 = self.ref_imgs_num-1 + + for step in tqdm.trange(self.gs_params.training_iterations): + + ### calculate loss between reference and rendered image from known view + loss = 0 + masked_rendered_img_batch = [] + masked_ref_img_batch = [] + masks_batch = [] + ref_masks_batch = [] + + step_ratio = min(1, step / self.gs_params.training_iterations) + + # update lr + self.renderer.gaussians.update_learning_rate(step) + + loss = 0 + + for _ in range(self.gs_params.batch_size): + ### calculate loss between reference and rendered image from known view + + i = random.randint(0, ref_imgs_num_minus_1) + + radius, elevation, azimuth, center_X, center_Y, center_Z = self.all_ref_cam_poses[i] + + orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32) + ref_pose = orbit_camera(elevation, azimuth, radius, target=orbit_target) + ref_cam = MiniCam(ref_pose, self.ref_size_W, self.ref_size_H, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, self.projection_matrix) + + bg_color = torch.tensor([1, 1, 1] if np.random.rand() > self.gs_params.invert_bg_prob else [0, 0, 0], dtype=torch.float32, device=self.device) + out = self.renderer.render(ref_cam, bg_color=bg_color) + + image = out["image"] # [3, H, W] in [0, 1] + mask = out["alpha"] # [1, H, W] in [0, 1] + + ref_mask = self.ref_masks_torch[i] + image_masked = (image * ref_mask).unsqueeze(0) + + masked_rendered_img_batch.append(image_masked) + masked_ref_img_batch.append(ref_imgs_masked[i]) + masks_batch.append(mask.unsqueeze(0)) + ref_masks_batch.append(ref_mask.unsqueeze(0)) + + masked_rendered_img_batch_torch = torch.cat(masked_rendered_img_batch, dim=0) + masked_ref_img_batch_torch = torch.cat(masked_ref_img_batch, dim=0) + masks_batch_torch = torch.cat(masks_batch, dim=0) + ref_masks_batch_torch = torch.cat(ref_masks_batch, dim=0) + + # rgb loss + loss += (1 - self.gs_params.lambda_ssim) * self.gs_params.loss_scale * step_ratio * F.mse_loss(masked_rendered_img_batch_torch, masked_ref_img_batch_torch) + + # alpha loss + loss += self.gs_params.lambda_alpha * self.gs_params.loss_scale * step_ratio * F.mse_loss(masks_batch_torch, ref_masks_batch_torch) + + # D-SSIM loss + # [1, 3, H, W] in [0, 1] + #loss += self.lambda_ssim * (1 - self.ssim_loss(X, Y)) + loss += self.gs_params.lambda_ssim * self.gs_params.loss_scale * step_ratio * (1 - self.ms_ssim_loss(masked_ref_img_batch_torch, masked_rendered_img_batch_torch)) + + if self.gs_params.lambda_offset > 0: + # Reference offset loss + offset_norm = self.renderer.gaussians.get_xyz_offset.norm(dim=-1, keepdim=True) + loss += self.gs_params.lambda_offset * self.gs_params.loss_scale * step_ratio * torch.mean(offset_norm) + + if self.gs_params.lambda_offset_opacity > 0: + # Alpha penalty loss + loss += self.gs_params.lambda_offset_opacity * self.gs_params.loss_scale * step_ratio * torch.mean(offset_norm.detach() * self.renderer.gaussians.get_opacity) + + + # optimize step + loss.backward() + self.optimizer.step() + self.optimizer.zero_grad() + + # densify and prune + if step >= self.gs_params.density_start_iter and step <= self.gs_params.density_end_iter: + viewspace_point_tensor, visibility_filter, radii = out["viewspace_points"], out["visibility_filter"], out["radii"] + self.renderer.gaussians.max_radii2D[visibility_filter] = torch.max(self.renderer.gaussians.max_radii2D[visibility_filter], radii[visibility_filter]) + self.renderer.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter) + + if step % self.gs_params.densification_interval == 0: + #self.renderer.gaussians.densify_and_prune(self.gs_params.densify_grad_threshold, min_opacity=0.01, extent=4, max_screen_size=1) + #self.renderer.gaussians.densify_and_prune_by_compatness(self.gs_params.K, min_opacity=0.01, extent=4, max_screen_size=1) + + #self.renderer.gaussians.densify_by_clone_and_split(self.gs_params.densify_grad_threshold, extent=4) + self.renderer.gaussians.densify_by_compatness(self.gs_params.K) + self.renderer.gaussians.prune(min_opacity=0.01, extent=4, max_screen_size=1, max_offset=0.1) + + if step % self.gs_params.opacity_reset_interval == 0: + self.renderer.gaussians.reset_opacity() + + ender.record() + torch.cuda.synchronize() + #t = starter.elapsed_time(ender) + + self.need_update = True \ No newline at end of file diff --git a/gaussian_splatting/main_3DGS_renderer.py b/gaussian_splatting/main_3DGS_renderer.py new file mode 100644 index 0000000..a637fad --- /dev/null +++ b/gaussian_splatting/main_3DGS_renderer.py @@ -0,0 +1,1057 @@ +import os +import math +import random +import numpy as np +from typing import NamedTuple, Optional +from torchtyping import TensorType +from plyfile import PlyData, PlyElement + +import torch +from torch import nn +import torch.nn.functional as F + +pytorch3d_capable = True +try: + import pytorch3d + from pytorch3d.ops import knn_points +except ImportError: + pytorch3d_capable = False + +from kornia.geometry.conversions import ( + quaternion_to_rotation_matrix, +) + +from diff_gaussian_rasterization import ( + GaussianRasterizationSettings, + GaussianRasterizer, +) +from simple_knn._C import distCUDA2 + +from ..shared_utils.sh_utils import eval_sh, SH2RGB, RGB2SH +from ..mesh_processer.mesh import Mesh +from ..mesh_processer.mesh_utils import decimate_mesh, clean_mesh + +def inverse_sigmoid(x): + return torch.log(x/(1-x)) + +def get_expon_lr_func( + lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 +): + + def helper(step): + if lr_init == lr_final: + # constant lr, ignore other params + return lr_init + if step < 0 or (lr_init == 0.0 and lr_final == 0.0): + # Disable this parameter + return 0.0 + if lr_delay_steps > 0: + # A kind of reverse cosine decay. + delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( + 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) + ) + else: + delay_rate = 1.0 + t = np.clip(step / max_steps, 0, 1) + log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return delay_rate * log_lerp + + return helper + + +def strip_lowerdiag(L): + uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") + + uncertainty[:, 0] = L[:, 0, 0] + uncertainty[:, 1] = L[:, 0, 1] + uncertainty[:, 2] = L[:, 0, 2] + uncertainty[:, 3] = L[:, 1, 1] + uncertainty[:, 4] = L[:, 1, 2] + uncertainty[:, 5] = L[:, 2, 2] + return uncertainty + +def strip_symmetric(sym): + return strip_lowerdiag(sym) + +def gaussian_3d_coeff(xyzs, covs): + # xyzs: [N, 3] + # covs: [N, 6] + x, y, z = xyzs[:, 0], xyzs[:, 1], xyzs[:, 2] + a, b, c, d, e, f = covs[:, 0], covs[:, 1], covs[:, 2], covs[:, 3], covs[:, 4], covs[:, 5] + + # eps must be small enough !!! + inv_det = 1 / (a * d * f + 2 * e * c * b - e**2 * a - c**2 * d - b**2 * f + 1e-24) + inv_a = (d * f - e**2) * inv_det + inv_b = (e * c - b * f) * inv_det + inv_c = (e * b - c * d) * inv_det + inv_d = (a * f - c**2) * inv_det + inv_e = (b * c - e * a) * inv_det + inv_f = (a * d - b**2) * inv_det + + power = -0.5 * (x**2 * inv_a + y**2 * inv_d + z**2 * inv_f) - x * y * inv_b - x * z * inv_c - y * z * inv_e + + power[power > 0] = -1e10 # abnormal values... make weights 0 + + return torch.exp(power) + +def build_rotation(r): + norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) + + q = r / norm[:, None] + + R = torch.zeros((q.size(0), 3, 3), device='cuda') + + r = q[:, 0] + x = q[:, 1] + y = q[:, 2] + z = q[:, 3] + + R[:, 0, 0] = 1 - 2 * (y*y + z*z) + R[:, 0, 1] = 2 * (x*y - r*z) + R[:, 0, 2] = 2 * (x*z + r*y) + R[:, 1, 0] = 2 * (x*y + r*z) + R[:, 1, 1] = 1 - 2 * (x*x + z*z) + R[:, 1, 2] = 2 * (y*z - r*x) + R[:, 2, 0] = 2 * (x*z - r*y) + R[:, 2, 1] = 2 * (y*z + r*x) + R[:, 2, 2] = 1 - 2 * (x*x + y*y) + return R + +def build_scaling_rotation(s, r): + L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") + R = build_rotation(r) + + L[:,0,0] = s[:,0] + L[:,1,1] = s[:,1] + L[:,2,2] = s[:,2] + + L = R @ L + return L + +def random_point_in_triangle(v0, v1, v2): + """ + Given three vertices v0, v1, v2, sample a point uniformly in the triangle + Algorithm Reference: + https://math.stackexchange.com/questions/538458/how-to-sample-points-on-a-triangle-surface-in-3d + https://stackoverflow.com/questions/47410054/generate-random-locations-within-a-triangular-domain + """ + + r1 = random.random() + r2 = random.random() + + s1 = math.sqrt(r1) + return v0 * (1.0 - s1) + v1 * (1.0 - r2) * s1 + v2 * r2 * s1# + +def find_points_within_radius(query_points, vertex_points, d): + """ + Finds vertex points within a given radius for each query point. + + Args: + query_points (torch.Tensor): Tensor of shape (P1, 3) representing query points. + vertex_points (torch.Tensor): Tensor of shape (P2, 3) representing vertex points. + d (float): Radius within which to search for vertex points. + + Returns: + List[List[int]]: A list of lists, where each inner list contains indices of vertex points + within the radius for the corresponding query point. + """ + num_query_points = query_points.shape[0] + + # Calculate pairwise distances between query points and vertex points + distances = torch.norm(query_points[:, None] - vertex_points, dim=2) + + # Create a mask indicating which vertex points are within the radius + mask = distances <= d + + # Collect indices of vertex points within the radius for each query point + result = [] + for i in range(num_query_points): + indices_within_radius = torch.nonzero(mask[i]).squeeze().tolist() + result.append(indices_within_radius) + + return result + +@torch.no_grad() +def K_nearest_neighbors( + points: torch.Tensor, + K: int, + query: Optional[torch.Tensor] = None, + return_dist=False, +): + if not pytorch3d_capable: + raise ImportError("pytorch3d is not installed, which is required for KNN") + + # query/points: Tensor of shape (N, P1/P2, D) giving a batch of N point clouds, each containing up to P1/P2 points of dimension D + if query is None: + query = points + dist, idx, nn = knn_points(query[None, ...], points[None, ...], K=K, return_nn=True) + + # idx: Tensor of shape (N, P1, K) + # nn: Tensor of shape (N, P1, K, D) + + # take the index 1 since index 0 is the point itself + if not return_dist: + return nn[0, :, 1:, :], idx[0, :, 1:] + else: + return nn[0, :, 1:, :], idx[0, :, 1:], dist[0, :, 1:] + + +def distance_to_gaussian_surface(points, svec, rotmat, query): + """ + Calculate the radius of the gaussian along the direction, which determined by offset between points and query + Calculation using Mahalanobis distance + + Args: + points (Tensor): gaussians position of shape (*, 3) + svec (Tensor): gaussians scale vector of shape (*, 3) + rotmat : the rotation matrix of shape (*, 3, 3) + query (Tensor): query positions of shape (*, 3) + + Returns: + Distance of shape (*, 1): + """ + offset_dir = query - points + + offset_dir = torch.einsum("bij,bj->bi", rotmat.transpose(-1, -2), offset_dir) + offset_dir = F.normalize(offset_dir, dim=-1) + z = offset_dir[..., 2] + y = offset_dir[..., 1] + x = offset_dir[..., 0] + r_xy = torch.sqrt(x**2 + y**2 + 1e-10) + cos_theta = z + sin_theta = r_xy + cos_phi = x / r_xy + sin_phi = y / r_xy + + d2 = svec[..., 0] ** 2 * cos_phi**2 + svec[..., 1] ** 2 * sin_phi**2 + #r2 = svec[..., 2] ** 2 * cos_theta**2 + d2**2 * sin_theta**2 + r2 = svec[..., 2] ** 2 * cos_theta**2 + d2 * sin_theta**2 # same as: squared_dist = np.dot(diff.T, np.dot(np.linalg.inv(covariance), diff)) + + """ + Alternatively: + + def gaussian_squared_distance(query_point, mean, covariance): + # Calculate the difference vector + diff = query_point - mean + + # Compute the squared Mahalanobis distance + inv_covariance = np.linalg.inv(covariance) + squared_dist = np.dot(diff.T, np.dot(inv_covariance, diff)) + + return squared_dist + + # Example usage + mean = np.array([x0, y0, z0]) # Gaussian mean (origin position) + covariance = np.diag([sigma_x**2, sigma_y**2, sigma_z**2]) # Covariance matrix + + query_point = np.array([x_q, y_q, z_q]) # Query point + squared_dist_to_gaussian = gaussian_squared_distance(query_point, mean, covariance) + + print(f"Squared distance to Gaussian surface: {squared_dist_to_gaussian:.4f}") + """ + + return torch.sqrt(r2 + 1e-10) + +def qvec2rotmat_batched(qvec: TensorType["N", 4]): + return quaternion_to_rotation_matrix(qvec) + +class BasicPointCloud(NamedTuple): + points: np.array + colors: np.array + normals: np.array + +class GaussianModel: + + def setup_functions(self): + def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): + L = build_scaling_rotation(scaling_modifier * scaling, rotation) + actual_covariance = L @ L.transpose(1, 2) + symm = strip_symmetric(actual_covariance) + return symm + + self.scaling_activation = torch.exp + self.scaling_inverse_activation = torch.log + + self.covariance_activation = build_covariance_from_scaling_rotation + + self.opacity_activation = torch.sigmoid + self.inverse_opacity_activation = inverse_sigmoid + + self.rotation_activation = torch.nn.functional.normalize + + + def __init__(self, sh_degree : int): + self.active_sh_degree = 0 + self.max_sh_degree = sh_degree + self.init_xyz = torch.empty(0) + self._xyz = torch.empty(0) + self._features_dc = torch.empty(0) # diffuse color + self._features_rest = torch.empty(0) # spherical harmonic coefficients + self._scaling = torch.empty(0) + self._rotation = torch.empty(0) + self._opacity = torch.empty(0) + self.max_radii2D = torch.empty(0) + self.xyz_gradient_accum = torch.empty(0) + self.denom = torch.empty(0) + self.optimizer = None + self.percent_dense = 0 + self.spatial_lr_scale = 0 + self.setup_functions() + + def capture(self): + return ( + self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + self.xyz_gradient_accum, + self.denom, + self.optimizer.state_dict(), + self.spatial_lr_scale, + ) + + def restore(self, model_args, training_args): + (self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + xyz_gradient_accum, + denom, + opt_dict, + self.spatial_lr_scale) = model_args + self.training_setup(training_args) + self.xyz_gradient_accum = xyz_gradient_accum + self.denom = denom + self.optimizer.load_state_dict(opt_dict) + + @property + def get_gaussians_num(self): + return self.init_xyz.shape[0] + + @property + def get_scaling(self): + return self.scaling_activation(self._scaling) + + @property + def get_rotation(self): + return self.rotation_activation(self._rotation) + + @property + def rotmat(self): + return qvec2rotmat_batched(self._rotation) + + @property + def get_xyz_offset(self): + return self.init_xyz - self._xyz + + @property + def get_xyz(self): + return self._xyz + + @property + def get_features(self): + features_dc = self._features_dc + features_rest = self._features_rest + return torch.cat((features_dc, features_rest), dim=1) + + @property + def get_opacity(self): + return self.opacity_activation(self._opacity) + + @torch.no_grad() + def extract_fields(self, resolution=128, num_blocks=16, relax_ratio=1.5): + # resolution: resolution of field + + block_size = 2 / num_blocks + + assert resolution % block_size == 0 + split_size = resolution // num_blocks + + opacities = self.get_opacity + + # pre-filter low opacity gaussians to save computation + mask = (opacities > 0.005).squeeze(1) + + opacities = opacities[mask] + xyzs = self.get_xyz[mask] + stds = self.get_scaling[mask] + + # normalize to ~ [-1, 1] + mn, mx = xyzs.amin(0), xyzs.amax(0) + self.center = (mn + mx) / 2 + self.scale = 1.8 / (mx - mn).amax().item() + + xyzs = (xyzs - self.center) * self.scale + stds = stds * self.scale + + covs = self.covariance_activation(stds, 1, self._rotation[mask]) + + # tile + device = opacities.device + occ = torch.zeros([resolution] * 3, dtype=torch.float32, device=device) + + X = torch.linspace(-1, 1, resolution).split(split_size) + Y = torch.linspace(-1, 1, resolution).split(split_size) + Z = torch.linspace(-1, 1, resolution).split(split_size) + + + # loop blocks (assume max size of gaussian is small than relax_ratio * block_size !!!) + for xi, xs in enumerate(X): + for yi, ys in enumerate(Y): + for zi, zs in enumerate(Z): + xx, yy, zz = torch.meshgrid(xs, ys, zs) + # sample points [M, 3] + pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).to(device) + # in-tile gaussians mask + vmin, vmax = pts.amin(0), pts.amax(0) + vmin -= block_size * relax_ratio + vmax += block_size * relax_ratio + mask = (xyzs < vmax).all(-1) & (xyzs > vmin).all(-1) + # if hit no gaussian, continue to next block + if not mask.any(): + continue + mask_xyzs = xyzs[mask] # [L, 3] + mask_covs = covs[mask] # [L, 6] + mask_opas = opacities[mask].view(1, -1) # [L, 1] --> [1, L] + + # query per point-gaussian pair. + g_pts = pts.unsqueeze(1).repeat(1, mask_covs.shape[0], 1) - mask_xyzs.unsqueeze(0) # [M, L, 3] + g_covs = mask_covs.unsqueeze(0).repeat(pts.shape[0], 1, 1) # [M, L, 6] + + # batch on gaussian to avoid OOM + batch_g = 1024 + val = 0 + for start in range(0, g_covs.shape[1], batch_g): + end = min(start + batch_g, g_covs.shape[1]) + w = gaussian_3d_coeff(g_pts[:, start:end].reshape(-1, 3), g_covs[:, start:end].reshape(-1, 6)).reshape(pts.shape[0], -1) # [M, l] + val += (mask_opas[:, start:end] * w).sum(-1) + + occ[xi * split_size: xi * split_size + len(xs), + yi * split_size: yi * split_size + len(ys), + zi * split_size: zi * split_size + len(zs)] = val.reshape(len(xs), len(ys), len(zs)) + + return occ + + def extract_mesh(self, path, density_thresh=1, resolution=128, decimate_target=1e5): + + os.makedirs(os.path.dirname(path), exist_ok=True) + + occ = self.extract_fields(resolution).detach().cpu().numpy() + + import mcubes + vertices, triangles = mcubes.marching_cubes(occ, density_thresh) + vertices = vertices / (resolution - 1.0) * 2 - 1 + + # transform back to the original space + vertices = vertices / self.scale + self.center.detach().cpu().numpy() + + vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.015) + if decimate_target > 0 and triangles.shape[0] > decimate_target: + vertices, triangles = decimate_mesh(vertices, triangles, decimate_target) + + v = torch.from_numpy(vertices.astype(np.float32)).contiguous().cuda() + f = torch.from_numpy(triangles.astype(np.int32)).contiguous().cuda() + + print( + f"[INFO] marching cubes result: {v.shape} ({v.min().item()}-{v.max().item()}), {f.shape}" + ) + + mesh = Mesh(v=v, f=f, device='cuda') + + return mesh + + def get_covariance(self, scaling_modifier = 1, gaussain_idx = None): + if gaussain_idx is None: + return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation) + else: + return self.covariance_activation(self.get_scaling[gaussain_idx], scaling_modifier, self._rotation[gaussain_idx]) + + def oneupSHdegree(self): + if self.active_sh_degree < self.max_sh_degree: + self.active_sh_degree += 1 + + def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float = 1): + self.spatial_lr_scale = spatial_lr_scale + fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() + fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) + features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() + features[:, :3, 0 ] = fused_color + features[:, 3:, 1:] = 0.0 + + print("Number of points at initialisation : ", fused_point_cloud.shape[0]) + + dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) + scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) + rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") + rots[:, 0] = 1 + + opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) + + self.init_xyz = fused_point_cloud.clone().detach() + self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) + self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) + self._scaling = nn.Parameter(scales.requires_grad_(True)) + self._rotation = nn.Parameter(rots.requires_grad_(True)) + self._opacity = nn.Parameter(opacities.requires_grad_(True)) + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def training_setup(self, training_args): + self.percent_dense = training_args.percent_dense + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + + l = [ + {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, + {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"}, + {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"}, + {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"}, + {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"}, + {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"} + ] + + self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) + self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale, + lr_final=training_args.position_lr_final*self.spatial_lr_scale, + lr_delay_mult=training_args.position_lr_delay_mult, + max_steps=training_args.position_lr_max_steps) + + def update_learning_rate(self, iteration): + ''' Learning rate scheduling per step ''' + for param_group in self.optimizer.param_groups: + if param_group["name"] == "xyz": + lr = self.xyz_scheduler_args(iteration) + param_group['lr'] = lr + return lr + + def construct_list_of_attributes(self): + l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] + # All channels except the 3 DC + for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): + l.append('f_dc_{}'.format(i)) + for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]): + l.append('f_rest_{}'.format(i)) + l.append('opacity') + for i in range(self._scaling.shape[1]): + l.append('scale_{}'.format(i)) + for i in range(self._rotation.shape[1]): + l.append('rot_{}'.format(i)) + return l + + def save_ply(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + + xyz = self._xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + opacities = self._opacity.detach().cpu().numpy() + scale = self._scaling.detach().cpu().numpy() + rotation = self._rotation.detach().cpu().numpy() + + dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()] + + elements = np.empty(xyz.shape[0], dtype=dtype_full) + attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1) + elements[:] = list(map(tuple, attributes)) + el = PlyElement.describe(elements, 'vertex') + PlyData([el]).write(path) + + def reset_opacity(self): + opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01)) + optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity") + self._opacity = optimizable_tensors["opacity"] + + def load_ply(self, path): + plydata = PlyData.read(path) + + xyz = np.stack((np.asarray(plydata.elements[0]["x"]), + np.asarray(plydata.elements[0]["y"]), + np.asarray(plydata.elements[0]["z"])), axis=1) + opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] + + print("Number of points at loading : ", xyz.shape[0]) + + features_dc = np.zeros((xyz.shape[0], 3, 1)) + features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) + features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"]) + features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"]) + + extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")] + assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3 + features_extra = np.zeros((xyz.shape[0], len(extra_f_names))) + for idx, attr_name in enumerate(extra_f_names): + features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name]) + # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC) + features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) + + scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] + scales = np.zeros((xyz.shape[0], len(scale_names))) + for idx, attr_name in enumerate(scale_names): + scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")] + rots = np.zeros((xyz.shape[0], len(rot_names))) + for idx, attr_name in enumerate(rot_names): + rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True)) + self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True)) + self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True)) + self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True)) + + self.active_sh_degree = self.max_sh_degree + + def create_from_mesh(self, mesh, num_pts): + """ + Sample gaussians uniformly for each face, this method can capture the structure of the 3D meshes well even for the fine structure e.g. fingers + """ + # In .obj file, first index is 1 instead of zero + vertices = np.concatenate([[[0., 0., 0.]], mesh.v.detach().cpu().numpy()]) + #normals = np.concatenate([[[0., 0., 0.]], mesh.n.detach().cpu().numpy()]) + + all_points = [] + num_pts_per_face = math.ceil(num_pts / mesh.f.shape[0]) + for triangle_face in mesh.f.detach().cpu().numpy(): + v0 = vertices[triangle_face[0]] + v1 = vertices[triangle_face[1]] + v2 = vertices[triangle_face[2]] + for i in range(num_pts_per_face): + all_points.append(random_point_in_triangle(v0, v1, v2)) + + num_pts = len(all_points) + + xyz = np.array(all_points) + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = BasicPointCloud( + points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) + ) + self.create_from_pcd(pcd, 10) + + def create_from_uv_data(self, uv_grids_data): + """ + Idealy this method should capture the most information for the texture, + but depends on the UV of 3D meshes, fine structure like fingers could have too little gaussians to form the proper structure, + also the number of gaussians is too big even for 1K texture + """ + if uv_grids_data is not None: + uv_coords, uv_coords_3d, uv_normals_3d = uv_grids_data + + num_pts = len(uv_coords_3d) + xyz = np.array(uv_coords_3d) + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = BasicPointCloud( + points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) + ) + self.create_from_pcd(pcd, 10) + + def replace_tensor_to_optimizer(self, tensor, name): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + if group["name"] == name: + stored_state = self.optimizer.state.get(group['params'][0], None) + stored_state["exp_avg"] = torch.zeros_like(tensor) + stored_state["exp_avg_sq"] = torch.zeros_like(tensor) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(tensor.requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def _prune_optimizer(self, mask): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + stored_state["exp_avg"] = stored_state["exp_avg"][mask] + stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask] + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True))) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def prune_points(self, mask): + valid_points_mask = ~mask + optimizable_tensors = self._prune_optimizer(valid_points_mask) + + self.init_xyz = self.init_xyz[valid_points_mask] + + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] + + self.denom = self.denom[valid_points_mask] + self.max_radii2D = self.max_radii2D[valid_points_mask] + + def cat_tensors_to_optimizer(self, tensors_dict): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + assert len(group["params"]) == 1 + extension_tensor = tensors_dict[group["name"]] + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + + stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0) + stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + + return optimizable_tensors + + def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation): + new_params = { + "xyz": new_xyz, + "f_dc": new_features_dc, + "f_rest": new_features_rest, + "opacity": new_opacities, + "scaling" : new_scaling, + "rotation" : new_rotation + } + + self.densify_with_new_params(new_params) + + def densify_with_new_params(self, new_params): + optimizable_tensors = self.cat_tensors_to_optimizer(new_params) + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def densify_and_split(self, grads, grad_threshold, scene_extent, N=2): + n_init_points = self.get_xyz.shape[0] + # Extract points that satisfy the gradient condition + padded_grad = torch.zeros((n_init_points), device="cuda") + padded_grad[:grads.shape[0]] = grads.squeeze() + selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent + ) + + stds = self.get_scaling[selected_pts_mask].repeat(N,1) + means =torch.zeros((stds.size(0), 3),device="cuda") + samples = torch.normal(mean=means, std=stds) + rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1) + new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1) + new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N)) + new_rotation = self._rotation[selected_pts_mask].repeat(N,1) + new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1) + new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1) + new_opacity = self._opacity[selected_pts_mask].repeat(N,1) + + # Update reference position for gaussians + self.init_xyz = torch.cat((self.init_xyz, self.init_xyz[selected_pts_mask].repeat(N,1)), dim=0) + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation) + + prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool))) + self.prune_points(prune_filter) + + def densify_and_clone(self, grads, grad_threshold, scene_extent): + # Extract points that satisfy the gradient condition + selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent + ) + + new_xyz = self._xyz[selected_pts_mask] + new_features_dc = self._features_dc[selected_pts_mask] + new_features_rest = self._features_rest[selected_pts_mask] + new_opacities = self._opacity[selected_pts_mask] + new_scaling = self._scaling[selected_pts_mask] + new_rotation = self._rotation[selected_pts_mask] + + # Update reference position for gaussians + self.init_xyz = torch.cat((self.init_xyz, self.init_xyz[selected_pts_mask]), dim=0) + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation) + + def densify_by_compatnes_with_idx(self, idx): + nn_scaling = self._scaling[idx] + nn_rotmat = self.rotmat[idx] + nn_pos = self._xyz[idx] + + nn_gaussian_surface_dist = distance_to_gaussian_surface( + nn_pos, nn_scaling, nn_rotmat, self._xyz + ) + gaussian_surface_dist = distance_to_gaussian_surface( + self._xyz, self._scaling, self.rotmat, nn_pos + ) + + dist_to_nn = torch.norm(nn_pos - self._xyz, dim=-1) + selected_pts_mask = (gaussian_surface_dist + nn_gaussian_surface_dist) < dist_to_nn + new_direction = (nn_pos - self._xyz) / dist_to_nn[..., None] + new_xyz = ( + self._xyz + + new_direction * (dist_to_nn + gaussian_surface_dist - nn_gaussian_surface_dist)[..., None] + / 2.0 + )[selected_pts_mask] + new_features_dc = self._features_dc[selected_pts_mask] + new_features_rest = self._features_rest[selected_pts_mask] + new_raw_alpha = self._opacity[selected_pts_mask] + new_raw_svec = self.scaling_inverse_activation( + torch.ones_like(self._scaling[selected_pts_mask]) + * (dist_to_nn - gaussian_surface_dist - nn_gaussian_surface_dist)[selected_pts_mask][..., None] + / 6.0 + ) + # print(torch.ones_like(self.svec.data[selected_pts_mask]).shape) + # print( + # (dist_to_nn - gaussian_surface_dist - nn_gaussian_surface_dist)[selected_pts_mask].shape + # ) + new_qvec = self._rotation[selected_pts_mask] + + # Update reference position for gaussians + self.init_xyz = torch.cat((self.init_xyz, self.init_xyz[selected_pts_mask]), dim=0) + + new_params = { + "xyz": new_xyz, + "f_dc": new_features_dc, + "f_rest": new_features_rest, + "opacity": new_raw_alpha, + "scaling": new_raw_svec, + "rotation": new_qvec + } + + return new_params + + def densify_by_compatness(self, K=1): + _, idx = K_nearest_neighbors(self._xyz, K=K+1) + + new_params_list = [] + for i in range(K): + new_params = self.densify_by_compatnes_with_idx(idx[:, i]) + new_params_list.append(new_params) + new_params = {} + for key in new_params_list[0].keys(): + new_params[key] = torch.cat([p[key] for p in new_params_list], dim=0) + + self.densify_with_new_params(new_params) + + #num_densified = new_params["xyz"].shape[0] + + def densify_by_clone_and_split(self, max_grad, extent): + grads = self.xyz_gradient_accum / self.denom + grads[grads.isnan()] = 0.0 + + self.densify_and_clone(grads, max_grad, extent) + self.densify_and_split(grads, max_grad, extent) + + def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size): + self.densify_by_clone_and_split(max_grad, extent) + self.prune(min_opacity, extent, max_screen_size) + + def densify_and_prune_by_compatness(self, K, min_opacity, extent, max_screen_size): + self.densify_by_compatness(K) + self.prune(min_opacity, extent, max_screen_size) + + def add_densification_stats(self, viewspace_point_tensor, update_filter): + self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True) + self.denom[update_filter] += 1 + + def prune(self, min_opacity, extent, max_screen_size, max_offset=0): + prune_mask = (self.get_opacity < min_opacity).squeeze() + if max_screen_size: + big_points_vs = self.max_radii2D > max_screen_size + big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent + #big_points_os = self.get_xyz_offset.norm(dim=1) > max_offset + #prune_mask = torch.logical_or(torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws), big_points_os) + prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws) + self.prune_points(prune_mask) + + torch.cuda.empty_cache() + +class Renderer: + def __init__(self, sh_degree=3, white_background=True, radius=1): + + self.sh_degree = sh_degree + self.white_background = white_background + self.radius = radius + + self.gaussians = GaussianModel(sh_degree) + + self.bg_color = torch.tensor( + [1, 1, 1] if white_background else [0, 0, 0], + dtype=torch.float32, + device="cuda", + ) + + def initialize(self, input=None, num_pts=5000, radius=0.5): + # load checkpoint + if isinstance(input, Mesh): + # load from 3D mesh + self.gaussians.create_from_mesh(input, num_pts) + elif input is None: + # init from random point cloud + + phis = np.random.random((num_pts,)) * 2 * np.pi + costheta = np.random.random((num_pts,)) * 2 - 1 + thetas = np.arccos(costheta) + mu = np.random.random((num_pts,)) + radius = radius * np.cbrt(mu) + x = radius * np.sin(thetas) * np.cos(phis) + y = radius * np.sin(thetas) * np.sin(phis) + z = radius * np.cos(thetas) + xyz = np.stack((x, y, z), axis=1) + # xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 + + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = BasicPointCloud( + points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) + ) + self.gaussians.create_from_pcd(pcd, 10) + elif isinstance(input, BasicPointCloud): + # load from a provided pcd + self.gaussians.create_from_pcd(input, 1) + else: + self.gaussians.create_from_uv_data(input) + + def render( + self, + viewpoint_camera, + scaling_modifier=1.0, + gaussain_idx=None, + bg_color=None, + override_color=None, + compute_cov3D_python=False, + convert_SHs_python=False, + ): + # Set up rasterization configuration + tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) + tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) + + raster_settings = GaussianRasterizationSettings( + image_height=int(viewpoint_camera.image_height), + image_width=int(viewpoint_camera.image_width), + tanfovx=tanfovx, + tanfovy=tanfovy, + bg=self.bg_color if bg_color is None else bg_color, + scale_modifier=scaling_modifier, + viewmatrix=viewpoint_camera.world_view_transform, + projmatrix=viewpoint_camera.full_proj_transform, + sh_degree=self.gaussians.active_sh_degree, + campos=viewpoint_camera.camera_center, + prefiltered=False, + debug=False, + ) + + rasterizer = GaussianRasterizer(raster_settings=raster_settings) + + gaussians_xyz = self.gaussians.get_xyz + gaussians_features = self.gaussians.get_features + gaussians_opacity = self.gaussians.get_opacity + + # If precomputed 3d covariance is provided, use it. If not, then it will be computed from + # scaling / rotation by the rasterizer. + gaussians_scales = None + gaussians_rotations = None + cov3D_precomp = None + if compute_cov3D_python: + cov3D_precomp = self.gaussians.get_covariance(scaling_modifier, gaussain_idx) + else: + gaussians_scales = self.gaussians.get_scaling + gaussians_rotations = self.gaussians.get_rotation + + if gaussain_idx is not None: + gaussians_xyz = gaussians_xyz[gaussain_idx] + gaussians_features = gaussians_features[gaussain_idx] + gaussians_opacity = gaussians_opacity[gaussain_idx] + if cov3D_precomp is None: + gaussians_scales = gaussians_scales[gaussain_idx] + gaussians_rotations = gaussians_rotations[gaussain_idx] + + # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means + screenspace_points = ( + torch.zeros_like( + gaussians_xyz, + dtype=gaussians_xyz.dtype, + requires_grad=True, + device="cuda", + ) + + 0 + ) + try: + screenspace_points.retain_grad() + except: + pass + + # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors + # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. + shs = None + colors_precomp = None + if override_color is None: + if convert_SHs_python: + shs_view = self.gaussians_features.transpose(1, 2).view( + -1, 3, (self.gaussians.max_sh_degree + 1) ** 2 + ) + dir_pp = gaussians_xyz - viewpoint_camera.camera_center.repeat( + gaussians_features.shape[0], 1 + ) + dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True) + sh2rgb = eval_sh( + self.gaussians.active_sh_degree, shs_view, dir_pp_normalized + ) + colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) + else: + shs = gaussians_features + else: + colors_precomp = override_color + + # Rasterize visible Gaussians to image, obtain their radii (on screen). + rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( + means3D=gaussians_xyz, + means2D=screenspace_points, + shs=shs, + colors_precomp=colors_precomp, + opacities=gaussians_opacity, + scales=gaussians_scales, + rotations=gaussians_rotations, + cov3D_precomp=cov3D_precomp, + ) + + rendered_image = rendered_image.clamp(0, 1) + + # Those Gaussians that were frustum culled or had a radius of 0 were not visible. + # They will be excluded from value updates used in the splitting criteria. + return { + "image": rendered_image, # rgb + "depth": rendered_depth, + "alpha": rendered_alpha, + "viewspace_points": screenspace_points, + "visibility_filter": radii > 0, + "radii": radii, + } \ No newline at end of file diff --git a/install.bat b/install.bat index a93ad66..df5a491 100644 --- a/install.bat +++ b/install.bat @@ -2,7 +2,6 @@ set "requirements_txt=%~dp0\requirements.txt" set "python_miniconda_exec=..\..\..\python_miniconda_env\ComfyUI\python.exe" -set "python_exec=..\..\..\python_embeded\python.exe" echo Installing ComfyUI-3D-Pack ... @@ -14,15 +13,12 @@ if exist "%python_miniconda_exec%" ( git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization "%python_miniconda_exec%" -s -m pip install ./diff-gaussian-rasterization "%python_miniconda_exec%" -s -m pip install ./simple-knn - "%python_miniconda_exec%" -s -m pip install git+https://github.com/NVlabs/nvdiffrast/ - "%python_miniconda_exec%" -s -m pip install git+https://github.com/ashawkey/kiuikit + git clone --recursive https://github.com/NVlabs/nvdiffrast + "%python_miniconda_exec%" -s -m pip install ./nvdiffrast + "%python_miniconda_exec%" -s -m pip install git+https://github.com/facebookresearch/pytorch3d.git@stable ) -else if exist "%python_exec%" ( - echo Installing with ComfyUI Portable - "%python_exec%" -s -m pip install -r "%requirements_txt%" -) else ( - echo Installing with system Python - pip install -r "%requirements_txt%" +else ( + echo ERROR: Cannot find Miniconda Environment "%python_miniconda_exec%" ) diff --git a/mesh_processer/mesh_utils.py b/mesh_processer/mesh_utils.py new file mode 100644 index 0000000..ca9fce9 --- /dev/null +++ b/mesh_processer/mesh_utils.py @@ -0,0 +1,147 @@ +import numpy as np +import pymeshlab as pml + + +def poisson_mesh_reconstruction(points, normals=None): + # points/normals: [N, 3] np.ndarray + + import open3d as o3d + + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(points) + + # outlier removal + pcd, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=10) + + # normals + if normals is None: + pcd.estimate_normals() + else: + pcd.normals = o3d.utility.Vector3dVector(normals[ind]) + + # visualize + o3d.visualization.draw_geometries([pcd], point_show_normal=False) + + mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson( + pcd, depth=9 + ) + vertices_to_remove = densities < np.quantile(densities, 0.1) + mesh.remove_vertices_by_mask(vertices_to_remove) + + # visualize + o3d.visualization.draw_geometries([mesh]) + + vertices = np.asarray(mesh.vertices) + triangles = np.asarray(mesh.triangles) + + print( + f"[INFO] poisson mesh reconstruction: {points.shape} --> {vertices.shape} / {triangles.shape}" + ) + + return vertices, triangles + + +def decimate_mesh( + verts, faces, target, backend="pymeshlab", remesh=False, optimalplacement=True +): + # optimalplacement: default is True, but for flat mesh must turn False to prevent spike artifect. + + _ori_vert_shape = verts.shape + _ori_face_shape = faces.shape + + if backend == "pyfqmr": + import pyfqmr + + solver = pyfqmr.Simplify() + solver.setMesh(verts, faces) + solver.simplify_mesh(target_count=target, preserve_border=False, verbose=False) + verts, faces, normals = solver.getMesh() + else: + m = pml.Mesh(verts, faces) + ms = pml.MeshSet() + ms.add_mesh(m, "mesh") # will copy! + + # filters + # ms.meshing_decimation_clustering(threshold=pml.Percentage(1)) + ms.meshing_decimation_quadric_edge_collapse( + targetfacenum=int(target), optimalplacement=optimalplacement + ) + + if remesh: + # ms.apply_coord_taubin_smoothing() + ms.meshing_isotropic_explicit_remeshing( + iterations=3, targetlen=pml.Percentage(1) + ) + + # extract mesh + m = ms.current_mesh() + verts = m.vertex_matrix() + faces = m.face_matrix() + + print( + f"[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}" + ) + + return verts, faces + + +def clean_mesh( + verts, + faces, + v_pct=1, + min_f=64, + min_d=20, + repair=True, + remesh=True, + remesh_size=0.01, +): + # verts: [N, 3] + # faces: [N, 3] + + _ori_vert_shape = verts.shape + _ori_face_shape = faces.shape + + m = pml.Mesh(verts, faces) + ms = pml.MeshSet() + ms.add_mesh(m, "mesh") # will copy! + + # filters + ms.meshing_remove_unreferenced_vertices() # verts not refed by any faces + + if v_pct > 0: + ms.meshing_merge_close_vertices( + threshold=pml.Percentage(v_pct) + ) # 1/10000 of bounding box diagonal + + ms.meshing_remove_duplicate_faces() # faces defined by the same verts + ms.meshing_remove_null_faces() # faces with area == 0 + + if min_d > 0: + ms.meshing_remove_connected_component_by_diameter( + mincomponentdiag=pml.Percentage(min_d) + ) + + if min_f > 0: + ms.meshing_remove_connected_component_by_face_number(mincomponentsize=min_f) + + if repair: + # ms.meshing_remove_t_vertices(method=0, threshold=40, repeat=True) + ms.meshing_repair_non_manifold_edges(method=0) + ms.meshing_repair_non_manifold_vertices(vertdispratio=0) + + if remesh: + # ms.apply_coord_taubin_smoothing() + ms.meshing_isotropic_explicit_remeshing( + iterations=3, targetlen=pml.AbsoluteValue(remesh_size) + ) + + # extract mesh + m = ms.current_mesh() + verts = m.vertex_matrix() + faces = m.face_matrix() + + print( + f"[INFO] mesh cleaning: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}" + ) + + return verts, faces diff --git a/nodes.py b/nodes.py index 3c929b8..a4e68d2 100644 --- a/nodes.py +++ b/nodes.py @@ -2,10 +2,12 @@ import os import re import folder_paths as comfy_paths +import torch from .diff_rast.diff_texturing import DiffTextureBaker from .shared_utils.common_utils import cstr from .mesh_processer.mesh import Mesh +from .gaussian_splatting.main_3DGS import GaussianSplatting, GSParams MANIFEST = { "name": "ComfyUI-3D-Pack", @@ -31,6 +33,7 @@ class Load_3D_Mesh: "resize": ("BOOLEAN", {"default": False},), "renormal": ("BOOLEAN", {"default": True},), "retex": ("BOOLEAN", {"default": False},), + "optimizable": ("BOOLEAN", {"default": False},), }, } @@ -43,7 +46,7 @@ class Load_3D_Mesh: FUNCTION = "load_mesh" CATEGORY = "ComfyUI3D/Import|Export" - def load_mesh(self, mesh_file_path, resize, renormal, retex): + def load_mesh(self, mesh_file_path, resize, renormal, retex, optimizable): mesh = None if not os.path.isabs(mesh_file_path): @@ -52,7 +55,8 @@ class Load_3D_Mesh: if os.path.exists(mesh_file_path): folder, filename = os.path.split(mesh_file_path) if filename.lower().endswith(SUPPORTED_3D_EXTENSIONS): - mesh = Mesh.load(mesh_file_path, resize, renormal, retex) + with torch.inference_mode(not optimizable): + mesh = Mesh.load(mesh_file_path, resize, renormal, retex) else: cstr(f"[{self.__class__.__name__}] File name {filename} does not end with supported 3D file extensions: {SUPPORTED_3D_EXTENSIONS}").error.print() else: @@ -86,12 +90,42 @@ class Save_3D_Mesh: os.makedirs(mesh_folder_path, exist_ok=True) if filename.lower().endswith(SUPPORTED_3D_EXTENSIONS): - mesh.export_mesh(mesh_save_path) + mesh.write(mesh_save_path) cstr(f"[{self.__class__.__name__}] saved model to {mesh_save_path}").msg.print() else: cstr(f"File name {filename} does not end with supported 3D file extensions: {SUPPORTED_3D_EXTENSIONS}").error.print() - return {"ui": {"mesh": mesh}} + return () + +class Save_3DGS: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "raw_3DGS": ("GS_RAW",), + "save_path": ("STRING", {"default": '3DGS_[time(%Y-%m-%d)].ply', "multiline": False}), + }, + } + + OUTPUT_NODE = True + RETURN_TYPES = () + FUNCTION = "save_3DGS" + CATEGORY = "ComfyUI3D/Import|Export" + + def save_3DGS(self, raw_3DGS, save_path): + + folder_path, filename = os.path.split(save_path) + + if not os.path.isabs(save_path): + folder_path = os.path.join(comfy_paths.output_directory, folder_path) + save_path = os.path.join(folder_path, filename) + + os.makedirs(folder_path, exist_ok=True) + + raw_3DGS.renderer.gaussians.save_ply(save_path) + + return () class Generate_Orbit_Camera_Poses: @@ -107,7 +141,6 @@ class Generate_Orbit_Camera_Poses: }, } - OUTPUT_NODE = True RETURN_TYPES = ( "ORBIT_CAMPOSES", # (orbit radius, elevation, azimuth, orbit center X, orbit center Y, orbit center Z) ) @@ -178,7 +211,39 @@ class Gaussian_Splatting: return { "required": { "reference_images": ("IMAGE",), + "reference_masks": ("MASK",), + "reference_orbit_camera_poses": ("ORBIT_CAMPOSES",), # (orbit radius, elevation, azimuth, orbit center X, orbit center Y, orbit center Z) + "reference_orbit_camera_fovy": ("FLOAT", {"default": 49.1, "min": 0.0, "max": 180.0, "step": 0.1}), + "training_iterations": ("INT", {"default": 1000, "min": 1, "max": 100000}), + "batch_size": ("INT", {"default": 5, "min": 1, "max": 0xffffffffffffffff}), + "loss_value_scale": ("FLOAT", {"default": 10000.0, }), + "ms_ssim_loss_weight": ("FLOAT", {"default": 0.2, }), + "alpha_loss_weight": ("FLOAT", {"default": 3, }), + "offset_loss_weight": ("FLOAT", {"default": 0.0, }), + "offset_opacity_loss_weight": ("FLOAT", {"default": 0.0, }), + "invert_background_probability": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}), + "feature_learning_rate": ("FLOAT", {"default": 0.01, }), + "opacity_learning_rate": ("FLOAT", {"default": 0.05, }), + "scaling_learning_rate": ("FLOAT", {"default": 0.005, }), + "rotation_learning_rate": ("FLOAT", {"default": 0.005, }), + "position_learning_rate_init": ("FLOAT", {"default": 0.001, }), + "position_learning_rate_final": ("FLOAT", {"default": 0.00002, }), + "position_learning_rate_delay_mult": ("FLOAT", {"default": 0.02, }), + "position_learning_rate_max_steps": ("INT", {"default": 500, }), + "initial_gaussians_num": ("INT", {"default": 5000, }), + "K_nearest_neighbors": ("INT", {"default": 3, }), + "percent_dense": ("FLOAT", {"default": 0.01, }), + "density_start_iterations": ("INT", {"default": 100, }), + "density_end_iterations": ("INT", {"default": 100000, }), + "densification_interval": ("INT", {"default": 100, }), + "opacity_reset_interval": ("INT", {"default": 700, }), + "densify_grad_threshold": ("FLOAT", {"default": 0.01 }), + "gaussian_sh_degree": ("INT", {"default": 0, }), }, + + "optional": { + "mesh_to_initialize_gaussian": ("MESH",), + } } RETURN_TYPES = ( @@ -190,8 +255,90 @@ class Gaussian_Splatting: FUNCTION = "run_3DGS" CATEGORY = "ComfyUI3D/Algorithm" - def run_3DGS(self, reference_images): + def run_3DGS(self, + reference_images, + reference_masks, + reference_orbit_camera_poses, + reference_orbit_camera_fovy, + training_iterations, + batch_size, + loss_value_scale, + ms_ssim_loss_weight, + alpha_loss_weight, + offset_loss_weight, + offset_opacity_loss_weight, + invert_background_probability, + feature_learning_rate, + opacity_learning_rate, + scaling_learning_rate, + rotation_learning_rate, + position_learning_rate_init, + position_learning_rate_final, + position_learning_rate_delay_mult, + position_learning_rate_max_steps, + initial_gaussians_num, + K_nearest_neighbors, + percent_dense, + density_start_iterations, + density_end_iterations, + densification_interval, + opacity_reset_interval, + densify_grad_threshold, + gaussian_sh_degree, + mesh_to_initialize_gaussian=None): + + raw_3DGS = None + + ref_imgs_num = len(reference_images) + ref_masks_num = len(reference_masks) + if ref_imgs_num == ref_masks_num: + + ref_cam_poses_num = len(reference_orbit_camera_poses) + if ref_imgs_num == ref_cam_poses_num: + + if batch_size > ref_imgs_num: + cstr(f"[{self.__class__.__name__}] Batch size {batch_size} is bigger than number of reference images {ref_imgs_num}! Set batch size to {ref_imgs_num} instead").warning.print() + batch_size = ref_imgs_num + + with torch.inference_mode(False): + + gs_params = GSParams(training_iterations, + batch_size, + loss_value_scale, + ms_ssim_loss_weight, + alpha_loss_weight, + offset_loss_weight, + offset_opacity_loss_weight, + invert_background_probability, + feature_learning_rate, + opacity_learning_rate, + scaling_learning_rate, + rotation_learning_rate, + position_learning_rate_init, + position_learning_rate_final, + position_learning_rate_delay_mult, + position_learning_rate_max_steps, + initial_gaussians_num, + K_nearest_neighbors, + percent_dense, + density_start_iterations, + density_end_iterations, + densification_interval, + opacity_reset_interval, + densify_grad_threshold, + gaussian_sh_degree) + + gs = GaussianSplatting(reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, gs_params, mesh_to_initialize_gaussian) + + gs.training() + + raw_3DGS = gs + + else: + cstr(f"[{self.__class__.__name__}] Number of reference images {ref_imgs_num} does not equal to number of reference camera poses {ref_cam_poses_num}").error.print() + else: + cstr(f"[{self.__class__.__name__}] Number of reference images {ref_imgs_num} does not equal to number of masks {ref_masks_num}").error.print() return (raw_3DGS, ) @@ -246,13 +393,15 @@ class Bake_Texture_To_Mesh: if batch_size > ref_imgs_num: cstr(f"[{self.__class__.__name__}] Batch size {batch_size} is bigger than number of reference images {ref_imgs_num}! Set batch size to {ref_imgs_num} instead").warning.print() batch_size = ref_imgs_num + + with torch.inference_mode(False): - texture_baker = DiffTextureBaker(reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, mesh, - training_iterations, batch_size, texture_learning_rate, train_mesh_geometry, geometry_learning_rate, ms_ssim_loss_weight, force_cuda_rasterize) - - texture_baker.training() - - trained_mesh, baked_texture = texture_baker.get_mesh_and_texture() + texture_baker = DiffTextureBaker(reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, mesh, + training_iterations, batch_size, texture_learning_rate, train_mesh_geometry, geometry_learning_rate, ms_ssim_loss_weight, force_cuda_rasterize) + + texture_baker.training() + + trained_mesh, baked_texture = texture_baker.get_mesh_and_texture() else: cstr(f"[{self.__class__.__name__}] Number of reference images {ref_imgs_num} does not equal to number of reference camera poses {ref_cam_poses_num}").error.print() diff --git a/requirements.txt b/requirements.txt index 767af68..ce532f5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,10 @@ -#python-dev-tools +#base cmake ninja numpy scipy opencv-python -tqdm +kornia # for training differentiable tensors pytorch_msssim @@ -13,4 +13,9 @@ pytorch_msssim trimesh plyfile pygltflib -xatlas \ No newline at end of file +xatlas +pymeshlab + +# extra +torchtyping +tqdm diff --git a/shared_utils/sh_utils.py b/shared_utils/sh_utils.py new file mode 100644 index 0000000..bbca7d1 --- /dev/null +++ b/shared_utils/sh_utils.py @@ -0,0 +1,118 @@ +# Copyright 2021 The PlenOctree Authors. +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import torch + +C0 = 0.28209479177387814 +C1 = 0.4886025119029199 +C2 = [ + 1.0925484305920792, + -1.0925484305920792, + 0.31539156525252005, + -1.0925484305920792, + 0.5462742152960396 +] +C3 = [ + -0.5900435899266435, + 2.890611442640554, + -0.4570457994644658, + 0.3731763325901154, + -0.4570457994644658, + 1.445305721320277, + -0.5900435899266435 +] +C4 = [ + 2.5033429417967046, + -1.7701307697799304, + 0.9461746957575601, + -0.6690465435572892, + 0.10578554691520431, + -0.6690465435572892, + 0.47308734787878004, + -1.7701307697799304, + 0.6258357354491761, +] + + +def eval_sh(deg, sh, dirs): + """ + Evaluate spherical harmonics at unit directions + using hardcoded SH polynomials. + Works with torch/np/jnp. + ... Can be 0 or more batch dimensions. + Args: + deg: int SH deg. Currently, 0-3 supported + sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2] + dirs: jnp.ndarray unit directions [..., 3] + Returns: + [..., C] + """ + assert deg <= 4 and deg >= 0 + coeff = (deg + 1) ** 2 + assert sh.shape[-1] >= coeff + + result = C0 * sh[..., 0] + if deg > 0: + x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3] + result = (result - + C1 * y * sh[..., 1] + + C1 * z * sh[..., 2] - + C1 * x * sh[..., 3]) + + if deg > 1: + xx, yy, zz = x * x, y * y, z * z + xy, yz, xz = x * y, y * z, x * z + result = (result + + C2[0] * xy * sh[..., 4] + + C2[1] * yz * sh[..., 5] + + C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] + + C2[3] * xz * sh[..., 7] + + C2[4] * (xx - yy) * sh[..., 8]) + + if deg > 2: + result = (result + + C3[0] * y * (3 * xx - yy) * sh[..., 9] + + C3[1] * xy * z * sh[..., 10] + + C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] + + C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] + + C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] + + C3[5] * z * (xx - yy) * sh[..., 14] + + C3[6] * x * (xx - 3 * yy) * sh[..., 15]) + + if deg > 3: + result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] + + C4[1] * yz * (3 * xx - yy) * sh[..., 17] + + C4[2] * xy * (7 * zz - 1) * sh[..., 18] + + C4[3] * yz * (7 * zz - 3) * sh[..., 19] + + C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] + + C4[5] * xz * (7 * zz - 3) * sh[..., 21] + + C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] + + C4[7] * xz * (xx - 3 * yy) * sh[..., 23] + + C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24]) + return result + +def RGB2SH(rgb): + return (rgb - 0.5) / C0 + +def SH2RGB(sh): + return sh * C0 + 0.5 \ No newline at end of file diff --git a/test.bat b/test.bat new file mode 100644 index 0000000..f8ae281 --- /dev/null +++ b/test.bat @@ -0,0 +1,5 @@ +set "python_miniconda_exec=..\..\..\python_miniconda_env\ComfyUI\python.exe" + +echo ERROR: Cannot find Miniconda Environment "%python_miniconda_exec%" + +pause \ No newline at end of file