diff --git a/README.md b/README.md
index 700c9ed..2073ebd 100644
--- a/README.md
+++ b/README.md
@@ -63,6 +63,19 @@
     - Azimuth: In top view, from angle 0 rotate 360 degree with step -90 you get (0, -90, -180/180, 90, 0), in this case camera rotates clock-wise, vice versa.
     - Elevation: 0 when camera points horizontally forward, pointing down to the ground is negitive angle, vice versa.
 
+- **FlexiCubes**: [nv-tlabs/FlexiCubes](https://github.com/nv-tlabs/FlexiCubes)
+  - Multi-View depth & mask (optional normal maps) as inputs
+  - Export to 3D Mesh
+  - Usage guide: 
+    - *voxel_grids_resolution*: determine mesh resolution/quality
+    - *depth_min_distance* *depth_max_distance* : distance from object to camera, object parts in the render that is closer(futher) to camera than depth_min_distance(depth_max_distance) will be rendered with pure white(black) RGB value 1, 1, 1(0, 0, 0)
+    - *mask_loss_weight*: Control the silhouette of reconstrocted 3D mesh
+    - *depth_loss_weight*: Control the shape of reconstrocted 3D mesh, this loss will also affect the mesh deform detail on the surface, so results depends on quality of the depth map
+    - *normal_loss_weight*: Optional. Use to refine the mesh deform detail on the surface
+    - *sdf_regularizer_weight*: Helps to remove floaters in areas of the shape that are not supervised by the application objective, such as internal faces when using image supervision only
+    - *remove_floaters_weight*: This can be increased if you observe artifacts in flat areas
+    - *cube_stabilizer_weight*: This does not have a significant impact during the optimization of a single shape, however it helps to stabilizing training in somecases
+
 - **Instant NGP**: [nerfacc](https://github.com/nerfstudio-project/nerfacc)
   - Multi-View images as inputs
   - Export to 3D Mesh using marching cubes
diff --git a/_Example_Workflows/[FlexiCubes] Multi-View Depth plus Optional Normal maps to 3D Mesh.json b/_Example_Workflows/[FlexiCubes] Multi-View Depth plus Optional Normal maps to 3D Mesh.json
new file mode 100644
index 0000000..c30d609
--- /dev/null
+++ b/_Example_Workflows/[FlexiCubes] Multi-View Depth plus Optional Normal maps to 3D Mesh.json	
@@ -0,0 +1,623 @@
+{
+  "last_node_id": 56,
+  "last_link_id": 104,
+  "nodes": [
+    {
+      "id": 9,
+      "type": "MaskToImage",
+      "pos": [
+        835,
+        -309
+      ],
+      "size": {
+        "0": 210,
+        "1": 26
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 97
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            4
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MaskToImage"
+      }
+    },
+    {
+      "id": 10,
+      "type": "PreviewImage",
+      "pos": [
+        1114,
+        -310
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {
+        "collapsed": true
+      },
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 4
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 19,
+      "type": "PreviewImage",
+      "pos": [
+        448,
+        -310
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {
+        "collapsed": true
+      },
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 89
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 54,
+      "type": "ImageToMask",
+      "pos": [
+        397,
+        249
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 95
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            96,
+            97
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageToMask"
+      },
+      "widgets_values": [
+        "red"
+      ]
+    },
+    {
+      "id": 27,
+      "type": "[Comfy3D] Stack Orbit Camera Poses",
+      "pos": [
+        897,
+        333
+      ],
+      "size": {
+        "0": 315,
+        "1": 586
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "orbit_camposes",
+          "type": "ORBIT_CAMPOSES",
+          "links": [
+            87
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "orbit_radius_list",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 6
+        },
+        {
+          "name": "elevation_list",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 6
+        },
+        {
+          "name": "azimuth_list",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 6
+        },
+        {
+          "name": "orbit_center_X_list",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 6
+        },
+        {
+          "name": "orbit_center_Y_list",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 6
+        },
+        {
+          "name": "orbit_center_Z_list",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Stack Orbit Camera Poses"
+      },
+      "widgets_values": [
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+        0,
+        0,
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+    },
+    {
+      "id": 55,
+      "type": "[Comfy3D] Preview 3DMesh",
+      "pos": [
+        2221,
+        53
+      ],
+      "size": [
+        600,
+        500
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh_file_path",
+          "type": "STRING",
+          "link": 100,
+          "widget": {
+            "name": "mesh_file_path"
+          }
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Preview 3DMesh"
+      },
+      "widgets_values": [
+        "",
+        null
+      ]
+    },
+    {
+      "id": 56,
+      "type": "[Comfy3D] Save 3D Mesh",
+      "pos": [
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+        54
+      ],
+      "size": {
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+        "1": 58
+      },
+      "flags": {},
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "link": 99
+        }
+      ],
+      "outputs": [
+        {
+          "name": "save_path",
+          "type": "STRING",
+          "links": [
+            100
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Save 3D Mesh"
+      },
+      "widgets_values": [
+        "FlexiCubes_Output\\Dragon_Test.obj"
+      ]
+    },
+    {
+      "id": 52,
+      "type": "PreviewImage",
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+        515,
+        549
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+      "flags": {
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 92
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 51,
+      "type": "LoadImagesFromDir //Inspire",
+      "pos": [
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+        441
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+      "size": {
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+      "flags": {},
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+        {
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+          "type": "INT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImagesFromDir //Inspire"
+      },
+      "widgets_values": [
+        "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\custom_nodes\\ComfyUI-3D-Pack\\_Example_Workflows\\_Example_Inputs_Files\\dragon_all_renders\\Normal",
+        0,
+        0
+      ]
+    },
+    {
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+      "type": "LoadImagesFromDir //Inspire",
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+      "size": {
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+        "1": 146
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
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+          "type": "IMAGE",
+          "links": [
+            89,
+            103
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+          "shape": 3,
+          "slot_index": 0
+        },
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+          "links": [],
+          "shape": 3,
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+        {
+          "name": "INT",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImagesFromDir //Inspire"
+      },
+      "widgets_values": [
+        "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\custom_nodes\\ComfyUI-3D-Pack\\_Example_Workflows\\_Example_Inputs_Files\\dragon_all_renders\\Depth",
+        0,
+        0
+      ]
+    },
+    {
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+        "1": 430
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+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "reference_depth_maps",
+          "type": "IMAGE",
+          "link": 103
+        },
+        {
+          "name": "reference_masks",
+          "type": "MASK",
+          "link": 96
+        },
+        {
+          "name": "reference_orbit_camera_poses",
+          "type": "ORBIT_CAMPOSES",
+          "link": 87
+        },
+        {
+          "name": "reference_normal_maps",
+          "type": "IMAGE",
+          "link": 104
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "links": [
+            99
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
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+        0.5,
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diff --git a/algorithms/flexicubes_renderer.py b/algorithms/flexicubes_renderer.py
new file mode 100644
index 0000000..e49211c
--- /dev/null
+++ b/algorithms/flexicubes_renderer.py
@@ -0,0 +1,74 @@
+import os
+import numpy as np
+import torch
+
+import nvdiffrast.torch as dr
+from kiui.cam import orbit_camera
+
+from ..flexicubes import util 
+
+class FlexiCubesRenderer:
+    def __init__(self, force_cuda_rast):
+
+        if force_cuda_rast or os.name != 'nt':
+            self.glctx = dr.RasterizeCudaContext()
+        else:
+            self.glctx = dr.RasterizeGLContext()
+
+    def get_rotate_camera(self, itr, fovy = np.deg2rad(45), iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda"):
+        proj_mtx = util.perspective(fovy, iter_res[1] / iter_res[0], cam_near_far[0], cam_near_far[1])
+
+        # Smooth rotation for display.
+        ang    = (itr / 10) * np.pi * 2
+        mv     = util.translate(0, 0, -cam_radius) @ (util.rotate_x(-0.4) @ util.rotate_y(ang))
+        mvp    = proj_mtx @ mv
+        return mv.to(device), mvp.to(device)
+        
+    def get_orbit_camera(self, azimuth=0, elevation=0, fovy=45, iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda"):
+        aspect = iter_res[1] / iter_res[0]
+        fovy = np.deg2rad(fovy)
+        proj_mtx = util.perspective(fovy, aspect, cam_near_far[0], cam_near_far[1])
+        #fovx = 2 * np.arctan(np.tan(fovy / 2) * aspect)
+        #proj_mtx = get_projection_matrix(cam_near_far[0], cam_near_far[1], fovx, -fovy, z_sign=-1.0)
+
+        # Smooth rotation for display.
+        #mv = util.translate(0, 0, -cam_radius) @ (util.rotate_x(-np.de2rad(elevation)) @ util.rotate_y(np.deg2rad(azimuth)))
+        mv = torch.tensor(np.linalg.inv(orbit_camera(-elevation, azimuth, cam_radius)))
+        mvp = proj_mtx @ mv
+        return mv.to(device), mvp.to(device)
+
+    def render_mesh(self, mesh, mv, mvp, iter_res, depth_min=-5.5, depth_max=-0.5, return_types=["mask", "depth", "normal"], white_bg=False):
+        '''
+        The rendering function used to produce the results in the paper.
+        '''
+        v_pos_clip = util.xfm_points(mesh.vertices.unsqueeze(0), mvp)  # Rotate it to camera coordinates
+        rast, db = dr.rasterize(
+            dr.RasterizeGLContext(), v_pos_clip, mesh.faces.int(), iter_res)
+
+        alpha_index = rast[..., -1:] > 0
+        inv_alpha_index = rast[..., -1:] <= 0
+        alpha = alpha_index.float()
+
+        out_dict = {}
+        for type in return_types:
+            if type == "mask" :
+                img = dr.antialias(alpha, rast, v_pos_clip, mesh.faces.int()) 
+            elif type == "depth":
+                v_pos_cam = util.xfm_points(mesh.vertices.unsqueeze(0), mv)
+                img, _ = util.interpolate(v_pos_cam[..., [2]].contiguous(), rast, mesh.faces.int())
+                img_masked = img[alpha_index]
+                img_masked = torch.clamp(img_masked, min=depth_min, max=depth_max)
+                img_masked = (img_masked - depth_min) / (depth_max - depth_min)
+                img[alpha_index] = img_masked
+                img[inv_alpha_index] = 0
+            elif type == "normal" :
+                normal_indices = (torch.arange(0, mesh.nrm.shape[0], dtype=torch.int64, device='cuda')[:, None]).repeat(1, 3)
+                img, _ = util.interpolate(mesh.nrm.unsqueeze(0).contiguous(), rast, normal_indices.int())
+            elif type == "vertex_normal":
+                img, _ = util.interpolate(mesh.v_nrm.unsqueeze(0).contiguous(), rast, mesh.faces.int())
+                img = dr.antialias((img + 1) * 0.5, rast, v_pos_clip, mesh.faces.int()) 
+            if white_bg:
+                bg = torch.ones_like(img) 
+                img = torch.lerp(bg, img, alpha)
+            out_dict[type] = img
+        return out_dict
diff --git a/algorithms/flexicubes_trainer.py b/algorithms/flexicubes_trainer.py
new file mode 100644
index 0000000..f666cb6
--- /dev/null
+++ b/algorithms/flexicubes_trainer.py
@@ -0,0 +1,212 @@
+import os
+import imageio
+
+import torch
+import numpy as np
+
+import tqdm
+import comfy.utils
+
+
+from .flexicubes_renderer import FlexiCubesRenderer
+from ..shared_utils.camera_utils import OrbitCamera
+from ..flexicubes.flexicubes import FlexiCubes
+from ..flexicubes.util import SimpleMesh
+from ..flexicubes.loss import sdf_reg_loss
+from ..mesh_processer.mesh import Mesh
+
+def lr_schedule(iter):
+    return max(0.0, 10**(-(iter)*0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs. 
+
+class FlexiCubesTrainer:
+    
+    def __init__(
+            self,
+            training_iterations,
+            batch_size,
+            learning_rate,
+            voxel_grids_resolution,
+            depth_near=0.5,
+            depth_far=5.5,
+            mask_loss_weight=1.0,
+            depth_loss_weight=100.0,
+            normal_loss_weight=1.0,
+            sdf_regularizer_weight=0.2,
+            remove_floaters_weight=0.5,
+            cube_stabilizer_weight=0.1,
+            force_cuda_rast=False,
+            device='cuda'
+        ):
+        self.device = torch.device(device)
+        
+        self.renderer = FlexiCubesRenderer(force_cuda_rast)
+
+        #  Create and initialize FlexiCubes
+        self.voxel_grid_res = voxel_grids_resolution
+        self.fc = FlexiCubes(self.device)
+        self.x_nx3, self.cube_fx8 = self.fc.construct_voxel_grid(self.voxel_grid_res)
+        self.x_nx3 *= 2 # scale up the grid so that it's larger than the target object
+        
+        self.sdf = torch.rand_like(self.x_nx3[:,0]) - 0.1 # randomly init SDF
+        self.sdf    = torch.nn.Parameter(self.sdf.clone().detach(), requires_grad=True)
+        # set per-cube learnable weights to zeros
+        self.weight = torch.zeros((self.cube_fx8.shape[0], 21), dtype=torch.float, device=self.device) 
+        self.weight    = torch.nn.Parameter(self.weight.clone().detach(), requires_grad=True)
+        self.deform = torch.nn.Parameter(torch.zeros_like(self.x_nx3), requires_grad=True)
+        
+        #  Retrieve all the edges of the voxel grid; these edges will be utilized to 
+        #  compute the regularization loss in subsequent steps of the process.    
+        all_edges = self.cube_fx8[:, self.fc.cube_edges].reshape(-1, 2)
+        self.grid_edges = torch.unique(all_edges, dim=0)
+        
+        #  Setup optimizer
+        self.optimizer = torch.optim.Adam([self.sdf, self.weight, self.deform], lr=learning_rate)
+        self.scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lambda x: lr_schedule(x)) 
+        
+        self.training_iterations = training_iterations
+        self.batch_size = batch_size
+        
+        self.depth_near = -depth_near
+        self.depth_far = -depth_far
+        
+        self.mask_loss_weight = mask_loss_weight
+        self.depth_loss_weight = depth_loss_weight
+        self.normal_loss_weight = normal_loss_weight
+        self.sdf_regularizer_weight = sdf_regularizer_weight
+        self.remove_floaters_weight = remove_floaters_weight
+        self.cube_stabilizer_weight = cube_stabilizer_weight
+    
+    def prepare_training(self, reference_depth_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, reference_normals=None):
+        self.ref_imgs_num = len(reference_depth_images)
+
+        self.all_ref_cam_poses = reference_orbit_camera_poses
+        self.ref_cam_fovy = reference_orbit_camera_fovy
+    
+        self.ref_size_H = reference_depth_images[0].shape[0]
+        self.ref_size_W = reference_depth_images[0].shape[1]
+        
+        self.cam = OrbitCamera(self.ref_size_W, self.ref_size_H, fovy=reference_orbit_camera_fovy)
+        
+        # prepare reference images and masks
+        self.ref_depth_imgs_torch = reference_depth_images[:, :, :, 0].unsqueeze(3).to(self.device) # (N, H, W, C) -> (N, H, W, 1)
+        self.ref_masks_torch = reference_masks.unsqueeze(3).to(self.device)
+        
+        if reference_normals is not None:
+            self.ref_normal_imgs_torch = (reference_normals * 2 - 1).to(self.device) # change value from [0, 1] -> [-1, 1]
+        else:
+            self.ref_normal_imgs_torch = None
+        
+        # prepare reference camera projection matrix for all camera poses
+        mv_all = []
+        mvp_all = []
+        for pose in self.all_ref_cam_poses:
+            mv, mvp = self.renderer.get_orbit_camera(pose[2], pose[1], cam_radius=pose[0], device=self.device)
+            mv_all.append(mv)
+            mvp_all.append(mvp)
+        self.mv_all = torch.stack(mv_all).to(self.device)
+        self.mvp_all = torch.stack(mvp_all).to(self.device)
+        self.camposes_len = len(self.all_ref_cam_poses)
+    
+    def training(self):
+        starter = torch.cuda.Event(enable_timing=True)
+        ender = torch.cuda.Event(enable_timing=True)
+        starter.record()
+        
+        comfy_pbar = comfy.utils.ProgressBar(self.training_iterations)
+        
+        if self.ref_normal_imgs_torch is not None:
+            return_types = ["mask", "depth", "normal"]
+        else:
+            return_types = ["mask", "depth"]
+        
+        for step in tqdm.trange(self.training_iterations):
+            # sample random render & camera pose from multi-views
+            batch_index = np.random.randint(0, self.camposes_len, size=self.batch_size)
+            mv = self.mv_all[batch_index, :, :]
+            mvp = self.mvp_all[batch_index, :, :]
+
+            target_mask = self.ref_masks_torch[batch_index, :, :, :]
+            target_depth = self.ref_depth_imgs_torch[batch_index, :, :, :]
+            
+            # extract and render FlexiCubes mesh
+            grid_verts = self.x_nx3 + (2-1e-8) / (self.voxel_grid_res * 2) * torch.tanh(self.deform)
+            vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20],
+                gamma_f=self.weight[:,20], training=True)
+            flexicubes_mesh = SimpleMesh(vertices, faces)
+            if self.ref_normal_imgs_torch is not None:
+                flexicubes_mesh.auto_normals()
+            buffers = self.renderer.render_mesh(flexicubes_mesh, mv, mvp, (self.ref_size_H, self.ref_size_W), self.depth_far, self.depth_near, return_types)
+            
+            t_iter = step / self.training_iterations
+            # evaluate reconstruction loss
+            # mask & depth shape: (N, H, W, 1)
+            mask_loss = (buffers['mask'] - target_mask).abs().mean() * self.mask_loss_weight
+            depth_loss = (((((buffers['depth'] - target_depth)* target_mask)**2).sum(-1)+1e-8)).sqrt().mean() * self.depth_loss_weight
+            total_loss = mask_loss + depth_loss
+            if self.ref_normal_imgs_torch is not None:
+                target_normal = self.ref_normal_imgs_torch[batch_index, :, :, :]
+                normal_loss = (((((buffers['normal'] - (target_normal))* target_mask)**2).sum(-1)+1e-8)).sqrt().mean() * self.normal_loss_weight * t_iter
+                total_loss += normal_loss
+        
+            sdf_weight = self.sdf_regularizer_weight - (self.sdf_regularizer_weight - self.sdf_regularizer_weight/20)*min(1.0, 4.0 * t_iter)
+            reg_loss = sdf_reg_loss(self.sdf, self.grid_edges).mean() * sdf_weight # Loss to eliminate internal floaters that are not visible
+            reg_loss += L_dev.mean() * self.remove_floaters_weight
+            reg_loss += (self.weight[:,:20]).abs().mean() * self.cube_stabilizer_weight
+            total_loss += reg_loss
+            
+            # optimize step
+            total_loss.backward()
+            self.optimizer.step()
+            self.scheduler.step()
+            self.optimizer.zero_grad()
+            
+            comfy_pbar.update_absolute(step + 1)
+            
+            #self.test_save(step, mv, mvp, grid_verts, total_loss, out_dir="C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\FlexiCubes_Output\\Test_Normals")
+            
+        ender.record()
+        
+    def get_mesh(self):
+        grid_verts = self.x_nx3 + (2-1e-8) / (self.voxel_grid_res * 2) * torch.tanh(self.deform)
+        vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20],
+            gamma_f=self.weight[:,20], training=False)
+
+        v = vertices.detach().contiguous().float().to(self.device)
+        f = faces.detach().contiguous().float().to(self.device)
+        mesh = Mesh(v=v, f=f, device=self.device)
+        mesh.auto_normal()
+        mesh.auto_uv()
+        
+        # Trimesh seems output better mesh
+        #import trimesh
+        #import os
+        #mesh_np = trimesh.Trimesh(vertices = vertices.detach().cpu().numpy(), faces=faces.detach().cpu().numpy(), process=False)
+        #mesh_np.export(os.path.join("C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\FlexiCubes_Output", 'output_trimesh.obj'))
+        
+        return mesh
+    
+    def test_save(self, step, mv, mvp, grid_verts, total_loss, out_dir, save_interval=20, display_res=[1024, 1024]):
+        if (step % save_interval == 0 or step == (self.training_iterations-1)): # save normal image for visualization
+            with torch.no_grad():
+                # extract mesh with training=False
+                vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20],
+                gamma_f=self.weight[:,20], training=False)
+                flexicubes_mesh = SimpleMesh(vertices, faces)
+                flexicubes_mesh.auto_normals() # compute face normals for visualization
+                
+                mv, mvp = self.renderer.get_rotate_camera(step//save_interval, iter_res=display_res, device=self.device)
+                mv = mv.unsqueeze(0)
+                mvp = mvp.unsqueeze(0)
+                val_buffers = self.renderer.render_mesh(flexicubes_mesh, mv, mvp, display_res, return_types=["normal"], white_bg=True)
+                val_image = ((val_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8)
+                #val_image = (val_buffers["depth"][0].detach().cpu().numpy()*255).astype(np.uint8)
+                #val_repeat = np.repeat(val_image, 4, axis=2)
+                #val_repeat[:, :, 3] = 255
+                
+                #gt_image = (gt_image[0].detach().cpu().numpy()*255).astype(np.uint8)
+                #gt_repeat = np.repeat(gt_image, 4, axis=2)
+                #gt_repeat[:, :, 3] = 255
+                
+                imageio.imwrite(os.path.join(out_dir, '{:04d}.png'.format(step)), val_image)
+                #imageio.imwrite(os.path.join(out_dir, '{:04d}.png'.format(step)), np.concatenate([val_repeat, gt_repeat], 1))
+                print(f"Optimization Step [{step}/{self.training_iterations}], Loss: {total_loss.item():.4f}")
\ No newline at end of file
diff --git a/algorithms/gaussian_model.txt b/algorithms/gaussian_model.txt
deleted file mode 100644
index e8de3f0..0000000
--- a/algorithms/gaussian_model.txt
+++ /dev/null
@@ -1,397 +0,0 @@
-import torch
-import numpy as np
-from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
-from torch import nn
-import os
-from utils.system_utils import mkdir_p
-from plyfile import PlyData, PlyElement
-from utils.sh_utils import RGB2SH
-from simple_knn._C import distCUDA2
-from utils.graphics_utils import BasicPointCloud
-from utils.general_utils import strip_symmetric, build_scaling_rotation
-
-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, divide_ratio : float):
-        self.active_sh_degree = 0
-        self.max_sh_degree = sh_degree  
-        self._xyz = torch.empty(0)
-        self._features_dc = torch.empty(0)
-        self._features_rest = torch.empty(0)
-        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()
-        self.divide_ratio = divide_ratio
-
-    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_scaling(self):
-        return self.scaling_activation(self._scaling)
-    
-    @property
-    def get_rotation(self):
-        return self.rotation_activation(self._rotation)
-    
-    @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)
-    
-    def get_covariance(self, scaling_modifier = 1):
-        return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)
-
-    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):
-        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._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']
-        
-        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):
-        mkdir_p(os.path.dirname(path))
-
-        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]
-
-        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_")]
-        extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
-        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])
-        
-        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_")]
-        scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
-        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")]
-        rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
-        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 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._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):
-        d = {"xyz": new_xyz,
-        "f_dc": new_features_dc,
-        "f_rest": new_features_rest,
-        "opacity": new_opacities,
-        "scaling" : new_scaling,
-        "rotation" : new_rotation}
-
-        optimizable_tensors = self.cat_tensors_to_optimizer(d)
-        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]
-        
-        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) / (self.divide_ratio*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)
-
-        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):
-        
-        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]
-
-        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)
-
-    def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
-        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)
-
-        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
-            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()
-
-    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
diff --git a/crm/util/flexicubes_geometry.py b/crm/util/flexicubes_geometry.py
index 31dff77..315a481 100644
--- a/crm/util/flexicubes_geometry.py
+++ b/crm/util/flexicubes_geometry.py
@@ -7,7 +7,7 @@
 # license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
 
 import torch
-from .flexicubes import FlexiCubes # replace later
+from flexicubes.flexicubes import FlexiCubes
 # from dmtet import sdf_reg_loss_batch
 import torch.nn.functional as F
 
diff --git a/flexicubes/__init__.py b/flexicubes/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/crm/util/flexicubes.py b/flexicubes/flexicubes.py
similarity index 100%
rename from crm/util/flexicubes.py
rename to flexicubes/flexicubes.py
diff --git a/flexicubes/loss.py b/flexicubes/loss.py
new file mode 100644
index 0000000..ba507f0
--- /dev/null
+++ b/flexicubes/loss.py
@@ -0,0 +1,95 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+import torch
+import torch_scatter
+
+###############################################################################
+# Pytorch implementation of the developability regularizer introduced in paper 
+# "Developability of Triangle Meshes" by Stein et al.
+###############################################################################
+def mesh_developable_reg(mesh):
+
+    verts = mesh.vertices
+    tris = mesh.faces
+
+    device = verts.device
+    V = verts.shape[0]
+    F = tris.shape[0]
+
+    POS_EPS = 1e-6
+    REL_EPS = 1e-6
+
+    def normalize(vecs):
+        return vecs / (torch.linalg.norm(vecs, dim=-1, keepdim=True) + POS_EPS)
+
+    tri_pos = verts[tris]
+
+    vert_normal_covariance_sum = torch.zeros((V, 9), device=device)
+    vert_area = torch.zeros(V, device=device)
+    vert_degree = torch.zeros(V, dtype=torch.int32, device=device)
+
+    for iC in range(3):  # loop over three corners of each triangle
+
+        # gather tri verts
+        pRoot = tri_pos[:, iC, :]
+        pA = tri_pos[:, (iC + 1) % 3, :]
+        pB = tri_pos[:, (iC + 2) % 3, :]
+
+        # compute the corner angle & normal
+        vA = pA - pRoot
+        vAn = normalize(vA)
+        vB = pB - pRoot
+        vBn = normalize(vB)
+        area_normal = torch.linalg.cross(vA, vB, dim=-1)
+        face_area = 0.5 * torch.linalg.norm(area_normal, dim=-1)
+        normal = normalize(area_normal)
+        corner_angle = torch.acos(torch.clamp(torch.sum(vAn * vBn, dim=-1), min=-1., max=1.))
+
+        # add up the contribution to the covariance matrix
+        outer = normal[:, :, None] @ normal[:, None, :]
+        contrib = corner_angle[:, None] * outer.reshape(-1, 9)
+
+        # scatter the result to the appropriate matrices
+        vert_normal_covariance_sum = torch_scatter.scatter_add(src=contrib,
+                                                               index=tris[:, iC],
+                                                               dim=-2,
+                                                               out=vert_normal_covariance_sum)
+
+        vert_area = torch_scatter.scatter_add(src=face_area / 3.,
+                                              index=tris[:, iC],
+                                              dim=-1,
+                                              out=vert_area)
+
+        vert_degree = torch_scatter.scatter_add(src=torch.ones(F, dtype=torch.int32, device=device),
+                                                index=tris[:, iC],
+                                                dim=-1,
+                                                out=vert_degree)
+
+    # The energy is the smallest eigenvalue of the outer-product matrix
+    vert_normal_covariance_sum = vert_normal_covariance_sum.reshape(
+        -1, 3, 3)  # reshape to a batch of matrices
+    vert_normal_covariance_sum = vert_normal_covariance_sum + torch.eye(
+        3, device=device)[None, :, :] * REL_EPS
+
+    min_eigvals = torch.min(torch.linalg.eigvals(vert_normal_covariance_sum).abs(), dim=-1).values
+
+    # Mask out degree-3 vertices
+    vert_area = torch.where(vert_degree == 3, torch.tensor(0, dtype=vert_area.dtype,device=vert_area.device), vert_area)
+
+    # Adjust the vertex area weighting so it is unit-less, and 1 on average
+    vert_area = vert_area * (V / torch.sum(vert_area, dim=-1, keepdim=True))
+
+    return vert_area * min_eigvals 
+
+def sdf_reg_loss(sdf, all_edges):
+    sdf_f1x6x2 = sdf[all_edges.reshape(-1)].reshape(-1,2)
+    mask = torch.sign(sdf_f1x6x2[...,0]) != torch.sign(sdf_f1x6x2[...,1])
+    sdf_f1x6x2 = sdf_f1x6x2[mask]
+    sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[...,0], (sdf_f1x6x2[...,1] > 0).float()) + \
+            torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[...,1], (sdf_f1x6x2[...,0] > 0).float())
+    return sdf_diff
\ No newline at end of file
diff --git a/crm/util/tables.py b/flexicubes/tables.py
similarity index 100%
rename from crm/util/tables.py
rename to flexicubes/tables.py
diff --git a/flexicubes/util.py b/flexicubes/util.py
new file mode 100644
index 0000000..e7de64f
--- /dev/null
+++ b/flexicubes/util.py
@@ -0,0 +1,93 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+import numpy as np
+import torch
+import nvdiffrast.torch as dr
+
+###############################################################################
+# Functions adapted from https://github.com/NVlabs/nvdiffrec
+###############################################################################
+
+def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    return torch.sum(x*y, -1, keepdim=True)
+
+def length(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor:
+    return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN
+
+def safe_normalize(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor:
+    return x / length(x, eps)
+
+def perspective(fovy=0.7854, aspect=1.0, n=0.1, f=1000.0, device=None):
+    y = np.tan(fovy / 2)
+    return torch.tensor([[1/(y*aspect),    0,            0,              0], 
+                         [           0, 1/-y,            0,              0], 
+                         [           0,    0, -(f+n)/(f-n), -(2*f*n)/(f-n)], 
+                         [           0,    0,           -1,              0]], dtype=torch.float32, device=device)
+
+def translate(x, y, z, device=None):
+    return torch.tensor([[1, 0, 0, x], 
+                         [0, 1, 0, y], 
+                         [0, 0, 1, z], 
+                         [0, 0, 0, 1]], dtype=torch.float32, device=device)
+
+@torch.no_grad()
+def random_rotation_translation(t, device=None):
+    m = np.random.normal(size=[3, 3])
+    m[1] = np.cross(m[0], m[2])
+    m[2] = np.cross(m[0], m[1])
+    m = m / np.linalg.norm(m, axis=1, keepdims=True)
+    m = np.pad(m, [[0, 1], [0, 1]], mode='constant')
+    m[3, 3] = 1.0
+    m[:3, 3] = np.random.uniform(-t, t, size=[3])
+    return torch.tensor(m, dtype=torch.float32, device=device)
+
+def rotate_x(a, device=None):
+    s, c = np.sin(a), np.cos(a)
+    return torch.tensor([[1,  0, 0, 0], 
+                         [0,  c, s, 0], 
+                         [0, -s, c, 0], 
+                         [0,  0, 0, 1]], dtype=torch.float32, device=device)
+
+def rotate_y(a, device=None):
+    s, c = np.sin(a), np.cos(a)
+    return torch.tensor([[ c, 0, s, 0], 
+                         [ 0, 1, 0, 0], 
+                         [-s, 0, c, 0], 
+                         [ 0, 0, 0, 1]], dtype=torch.float32, device=device)
+    
+class SimpleMesh:
+    def __init__(self, vertices, faces):
+        self.vertices = vertices
+        self.faces = faces
+        
+    def auto_normals(self):
+        v0 = self.vertices[self.faces[:, 0], :]
+        v1 = self.vertices[self.faces[:, 1], :]
+        v2 = self.vertices[self.faces[:, 2], :]
+        nrm = safe_normalize(torch.cross(v1 - v0, v2 - v0, dim=-1))
+        self.nrm = nrm
+
+def xfm_points(points, matrix):
+    '''Transform points.
+    Args:
+        points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3]
+        matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4]
+        use_python: Use PyTorch's torch.matmul (for validation)
+    Returns:
+        Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4].
+    '''
+    out = torch.matmul(
+        torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2))
+    if torch.is_anomaly_enabled():
+        assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN"
+    return out
+
+def interpolate(attr, rast, attr_idx, rast_db=None):
+    return dr.interpolate(
+        attr, rast, attr_idx, rast_db=rast_db,
+        diff_attrs=None if rast_db is None else 'all')
\ No newline at end of file
diff --git a/mesh_processer/mesh.py b/mesh_processer/mesh.py
index 8def0bf..39add52 100644
--- a/mesh_processer/mesh.py
+++ b/mesh_processer/mesh.py
@@ -832,10 +832,10 @@ class Mesh:
             fp.write(f"newmtl defaultMat \n")
             fp.write(f"Ka 1 1 1 \n")
             fp.write(f"Kd 1 1 1 \n")
-            fp.write(f"Ks 0 0 0 \n")
+            fp.write(f"Ks 1 1 1 \n")
             #fp.write(f"Tr 1 \n") # will cause the mesh materials in three.js completely transparent
             fp.write(f"illum 1 \n")
-            fp.write(f"Ns 0 \n")
+            fp.write(f"Ns 10 \n")
             if self.albedo is not None:
                 fp.write(f"map_Kd {os.path.basename(albedo_path)} \n")
             if self.metallicRoughness is not None:
diff --git a/nodes.py b/nodes.py
index c6a6193..a3322fa 100644
--- a/nodes.py
+++ b/nodes.py
@@ -40,6 +40,8 @@ from .algorithms.nerf_marching_cubes_converter import GSConverterNeRFMarchingCub
 from .algorithms.NeuS_runner import NeuSParams, NeuSRunner
 from .algorithms.convolutional_reconstruction_model import CRMSampler
 from .algorithms.Instant_NGP import InstantNGP
+from .algorithms.flexicubes_trainer import FlexiCubesTrainer
+
 from .tgs.utils.config import ExperimentConfig, load_config as load_config_tgs
 from .tgs.data import CustomImageOrbitDataset
 from .tgs.utils.misc import todevice, get_device
@@ -2000,10 +2002,98 @@ class Instant_NGP:
             v = torch.from_numpy(vertices).contiguous().float().to(device)
             f = torch.from_numpy(triangles).contiguous().int().to(device)
 
-            mesh = Mesh(v=v, f=f, albedo=None, device=device)
+            mesh = Mesh(v=v, f=f, device=device)
             mesh.auto_normal()
             mesh.auto_uv()
             
             mesh.albedo = color_func_to_albedo(mesh, ngp.get_color, texture_resolution, device=device, force_cuda_rast=force_cuda_rast)
             
+            return (mesh, )
+        
+class FlexiCubes_MVS:
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "reference_depth_maps": ("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": 512, "min": 1, "max": 0xffffffffffffffff}),
+                "batch_size": ("INT", {"default": 4, "min": 1, "max": 0xffffffffffffffff}),
+                "learning_rate": ("FLOAT", {"default": 0.01, "min": 0.001, "step": 0.001}),
+                "voxel_grids_resolution": ("INT", {"default": 128, "min": 1, "max": 0xffffffffffffffff}),
+                "depth_min_distance": ("FLOAT", {"default": 0.5, "min": 0.0, "step": 0.01}),
+                "depth_max_distance": ("FLOAT", {"default": 5.5, "min": 0.0, "step": 0.01}),
+                "mask_loss_weight": ("FLOAT", {"default": 1.0, "min": 0.01, "step": 0.01}),
+                "depth_loss_weight": ("FLOAT", {"default": 100.0, "min": 0.01, "step": 0.01}),
+                "normal_loss_weight": ("FLOAT", {"default": 1.0, "min": 0.01, "step": 0.01}),
+                "sdf_regularizer_weight": ("FLOAT", {"default": 0.2, "min": 0.01, "step": 0.01}),
+                "remove_floaters_weight": ("FLOAT", {"default": 0.5, "min": 0.01, "step": 0.01}),
+                "cube_stabilizer_weight": ("FLOAT", {"default": 0.1, "min": 0.01, "step": 0.01}),
+                "force_cuda_rast": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "reference_normal_maps": ("IMAGE",), 
+            }
+        }
+
+    RETURN_TYPES = (
+        "MESH",
+    )
+    RETURN_NAMES = (
+        "mesh",
+    )
+    FUNCTION = "run_flexicubes"
+    CATEGORY = "Comfy3D/Algorithm"
+    
+    def run_flexicubes(
+        self,
+        reference_depth_maps,
+        reference_masks,
+        reference_orbit_camera_poses,
+        reference_orbit_camera_fovy,
+        training_iterations,
+        batch_size,
+        learning_rate,
+        voxel_grids_resolution,
+        depth_min_distance,
+        depth_max_distance,
+        mask_loss_weight,
+        depth_loss_weight,
+        normal_loss_weight,
+        sdf_regularizer_weight,
+        remove_floaters_weight,
+        cube_stabilizer_weight,
+        force_cuda_rast,
+        reference_normal_maps=None
+    ):
+        
+        with torch.inference_mode(False):
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+            
+            fc_trainer = FlexiCubesTrainer(
+                training_iterations,
+                batch_size,
+                learning_rate,
+                voxel_grids_resolution,
+                depth_min_distance,
+                depth_max_distance,
+                mask_loss_weight,
+                depth_loss_weight,
+                normal_loss_weight,
+                sdf_regularizer_weight,
+                remove_floaters_weight,
+                cube_stabilizer_weight,
+                force_cuda_rast,
+                device=device
+            )
+            
+            fc_trainer.prepare_training(reference_depth_maps, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, reference_normal_maps)
+            
+            fc_trainer.training()
+            
+            mesh = fc_trainer.get_mesh()
+            
             return (mesh, )
\ No newline at end of file
diff --git a/shared_utils/camera_utils.py b/shared_utils/camera_utils.py
index af73e8d..b6f1974 100644
--- a/shared_utils/camera_utils.py
+++ b/shared_utils/camera_utils.py
@@ -151,14 +151,12 @@ class OrbitCamera:
 def calculate_fovX(H, W, fovy):
     return 2 * np.arctan(np.tan(fovy / 2) * W / H)
         
-def get_projection_matrix(znear, zfar, fovX, fovY):
+def get_projection_matrix(znear, zfar, fovX, fovY, z_sign=1.0):
     tanHalfFovY = math.tan((fovY / 2))
     tanHalfFovX = math.tan((fovX / 2))
 
     P = torch.zeros(4, 4)
 
-    z_sign = 1.0
-
     P[0, 0] = 1 / tanHalfFovX
     P[1, 1] = 1 / tanHalfFovY
     P[3, 2] = z_sign
diff --git a/web/html/gsVisualizer.html b/web/html/gsVisualizer.html
index b121d1e..0699e6f 100644
--- a/web/html/gsVisualizer.html
+++ b/web/html/gsVisualizer.html
@@ -22,7 +22,7 @@
 		<script type="importmap">
 			{
 			  "imports": {
-				"gsplat": "https://unpkg.com/gsplat@latest/dist/index.js"
+				"gsplat": "https://cdn.jsdelivr.net/npm/gsplat@latest/dist/index.js"
 			  }
 			}
 		</script>
diff --git a/web/html/threeVisualizer.html b/web/html/threeVisualizer.html
index 55f0fe2..74fb9ed 100644
--- a/web/html/threeVisualizer.html
+++ b/web/html/threeVisualizer.html
@@ -22,8 +22,8 @@
 			<script type="importmap">
 				{
 				  "imports": {
-					"three": "https://unpkg.com/three@latest/build/three.module.js",
-					"three/addons/": "https://unpkg.com/three@latest/examples/jsm/"
+					"three": "https://cdn.jsdelivr.net/npm/three@latest/build/three.module.js",
+					"three/addons/": "https://cdn.jsdelivr.net/npm/three@latest/examples/jsm/"
 				  }
 				}
 			</script>
diff --git a/web/js/threeVisualizer.js b/web/js/threeVisualizer.js
index eb2101a..dbe6641 100644
--- a/web/js/threeVisualizer.js
+++ b/web/js/threeVisualizer.js
@@ -28,7 +28,7 @@ const scene = new THREE.Scene();
 scene.background = new THREE.Color( 0x000000 );
 scene.environment = pmremGenerator.fromScene( new RoomEnvironment( renderer ), 0.04 ).texture;
 
-const ambientLight = new THREE.AmbientLight( 0xffffff );
+const ambientLight = new THREE.AmbientLight( 0xffffff , 3.0 );
 
 const camera = new THREE.PerspectiveCamera( 40, window.innerWidth / window.innerHeight, 1, 100 );
 camera.position.set( 5, 2, 8 );