ImpactFrames
ef48827af9
fix some issues with nvdiffrast need to track were the gradient propagation is getting cut I think is on _fill_holes
507 lines
20 KiB
Python
507 lines
20 KiB
Python
import torch
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import torch.nn.functional as F
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import numpy as np
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import utils3d
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import nvdiffrast.torch as dr
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from tqdm import tqdm
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import trimesh
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import trimesh.visual
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import xatlas
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import pyvista as pv
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from pymeshfix import _meshfix
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import igraph
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import cv2
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from PIL import Image
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from typing import Union, List
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from .random_utils import sphere_hammersley_sequence
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from .render_utils import render_multiview
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from ..representations import Strivec, Gaussian, MeshExtractResult
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import gc
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@torch.no_grad()
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def _fill_holes(
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verts: torch.Tensor,
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faces: torch.Tensor,
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max_hole_size=0.04,
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max_hole_nbe=32,
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resolution=128,
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num_views=500,
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debug=False,
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verbose=False
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):
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"""
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Rasterize a mesh from multiple views and remove invisible faces.
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Also includes postprocessing to:
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1. Remove connected components that have low visibility.
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2. Apply a mincut to remove inner faces connected to outer faces by a small hole.
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This function relies on torch tensors on GPU and ensures compatibility with nvdiffrast.
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Args:
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verts (torch.Tensor): Vertices of the mesh. Shape (V, 3), float32, cuda.
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faces (torch.Tensor): Faces of the mesh. Shape (F, 3), int32, cuda.
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max_hole_size (float): Maximum area of a hole to fill.
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max_hole_nbe (int): Maximum boundary edges for small hole fill.
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resolution (int): Rasterization resolution.
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num_views (int): Number of random views used for visibility checks.
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debug (bool): If True, save debugging info.
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verbose (bool): If True, print progress info.
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"""
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yaws = []
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pitchs = []
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for i in range(num_views):
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y, p = sphere_hammersley_sequence(i, num_views)
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yaws.append(y)
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pitchs.append(p)
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yaws = torch.tensor(yaws, device=verts.device)
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pitchs = torch.tensor(pitchs, device=verts.device)
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radius = 2.0
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fov = torch.deg2rad(torch.tensor(40.0, device=verts.device))
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projection = utils3d.torch.perspective_from_fov_xy(fov, fov, 1, 3).to(verts.device)
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views = []
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for (yaw, pitch) in zip(yaws, pitchs):
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orig = torch.tensor([
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torch.sin(yaw) * torch.cos(pitch),
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torch.cos(yaw) * torch.cos(pitch),
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torch.sin(pitch),
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], device=verts.device).float() * radius
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view = utils3d.torch.view_look_at(
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orig,
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torch.tensor([0, 0, 0], device=verts.device).float(),
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torch.tensor([0, 0, 1], device=verts.device).float()
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)
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views.append(view)
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views = torch.stack(views, dim=0)
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visblity = torch.zeros(faces.shape[0], dtype=torch.int32, device=verts.device)
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rastctx = utils3d.torch.RastContext(backend='cuda')
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for i in tqdm(range(views.shape[0]), disable=not verbose, desc='Rasterizing'):
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view = views[i]
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buffers = utils3d.torch.rasterize_triangle_faces(
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rastctx, verts[None], faces, resolution, resolution,
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view=view, projection=projection
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)
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face_id = buffers['face_id'][0][buffers['mask'][0] > 0.95] - 1
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face_id = torch.unique(face_id).long()
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visblity[face_id] += 1
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visblity = visblity.float() / num_views
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edges, face2edge, edge_degrees = utils3d.torch.compute_edges(faces)
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boundary_edge_indices = torch.nonzero(edge_degrees == 1).reshape(-1)
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connected_components = utils3d.torch.compute_connected_components(faces, edges, face2edge)
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outer_face_indices = torch.zeros(faces.shape[0], dtype=torch.bool, device=faces.device)
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for i in range(len(connected_components)):
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cc_vis = visblity[connected_components[i]]
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outer_face_indices[connected_components[i]] = cc_vis > min(max(cc_vis.quantile(0.75).item(), 0.25), 0.5)
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outer_face_indices = outer_face_indices.nonzero().reshape(-1)
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inner_face_indices = torch.nonzero(visblity == 0).reshape(-1)
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if verbose:
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tqdm.write(f'Found {inner_face_indices.shape[0]} invisible faces')
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if inner_face_indices.numel() == 0:
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return verts, faces
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dual_edges, dual_edge2edge = utils3d.torch.compute_dual_graph(face2edge)
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dual_edge2edge = edges[dual_edge2edge]
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dual_edges_weights = torch.norm(verts[dual_edge2edge[:, 0]] - verts[dual_edge2edge[:, 1]], dim=1)
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if verbose:
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tqdm.write(f'Dual graph: {dual_edges.shape[0]} edges')
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g = igraph.Graph()
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g.add_vertices(faces.shape[0])
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g.add_edges(dual_edges.cpu().numpy())
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g.es['weight'] = dual_edges_weights.cpu().numpy()
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g.add_vertex('s')
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g.add_vertex('t')
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g.add_edges([(f.item(), 's') for f in inner_face_indices], attributes={'weight': np.ones(inner_face_indices.shape[0])})
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g.add_edges([(f.item(), 't') for f in outer_face_indices], attributes={'weight': np.ones(outer_face_indices.shape[0])})
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cut = g.mincut('s', 't', (np.array(g.es['weight']) * 1000).tolist())
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remove_face_indices = torch.tensor([v for v in cut.partition[0] if v < faces.shape[0]], dtype=torch.long, device=faces.device)
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if verbose:
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tqdm.write('Mincut solved, checking the cut')
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to_remove_cc = utils3d.torch.compute_connected_components(faces[remove_face_indices])
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valid_remove_cc = []
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for cc in to_remove_cc:
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vis_median = visblity[remove_face_indices[cc]].median()
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if vis_median > 0.25:
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continue
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cc_edge_indices, cc_edges_degree = torch.unique(face2edge[remove_face_indices[cc]], return_counts=True)
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cc_boundary_edge_indices = cc_edge_indices[cc_edges_degree == 1]
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cc_new_boundary_edge_indices = cc_boundary_edge_indices[~torch.isin(cc_boundary_edge_indices, boundary_edge_indices)]
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if len(cc_new_boundary_edge_indices) > 0:
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cc_new_boundary_edge_cc = utils3d.torch.compute_edge_connected_components(edges[cc_new_boundary_edge_indices])
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cc_new_boundary_edges_cc_area = []
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for edge_cc in cc_new_boundary_edge_cc:
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center = verts[edges[cc_new_boundary_edge_indices[edge_cc]]].mean(dim=(0, 1))
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e1 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 0]] - center
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e2 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 1]] - center
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area = torch.norm(torch.cross(e1, e2, dim=-1), dim=1).sum() * 0.5
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cc_new_boundary_edges_cc_area.append(area)
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if any(a > max_hole_size for a in cc_new_boundary_edges_cc_area):
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continue
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valid_remove_cc.append(cc)
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if len(valid_remove_cc) > 0:
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remove_face_indices = remove_face_indices[torch.cat(valid_remove_cc)]
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mask = torch.ones(faces.shape[0], dtype=torch.bool, device=faces.device)
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mask[remove_face_indices] = 0
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faces = faces[mask]
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faces, verts = utils3d.torch.remove_unreferenced_vertices(faces, verts)
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if verbose:
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tqdm.write(f'Removed {(~mask).sum()} faces by mincut')
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else:
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if verbose:
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tqdm.write('Removed 0 faces by mincut')
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mesh = _meshfix.PyTMesh()
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mesh.load_array(verts.cpu().numpy(), faces.cpu().numpy())
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mesh.fill_small_boundaries(nbe=max_hole_nbe, refine=True)
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v, f = mesh.return_arrays()
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verts = torch.tensor(v, device='cuda', dtype=torch.float32)
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faces = torch.tensor(f, device='cuda', dtype=torch.int32)
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return verts, faces
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def postprocess_mesh(
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vertices: np.ndarray,
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faces: np.ndarray,
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simplify: bool = True,
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simplify_ratio: float = 0.9,
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fill_holes: bool = True,
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fill_holes_max_hole_size: float = 0.04,
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fill_holes_max_hole_nbe: int = 32,
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fill_holes_resolution: int = 1024,
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fill_holes_num_views: int = 1000,
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debug: bool = False,
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verbose: bool = False,
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):
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if verbose:
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tqdm.write(f'Before postprocess: {vertices.shape[0]} vertices, {faces.shape[0]} faces')
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if simplify and simplify_ratio > 0:
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mesh = pv.PolyData(vertices, np.concatenate([np.full((faces.shape[0], 1), 3), faces], axis=1))
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mesh = mesh.decimate(simplify_ratio, progress_bar=verbose)
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vertices, faces = mesh.points, mesh.faces.reshape(-1, 4)[:, 1:]
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if verbose:
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tqdm.write(f'After decimate: {vertices.shape[0]} vertices, {faces.shape[0]} faces')
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if fill_holes:
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vertices_t = torch.tensor(vertices, device='cuda', dtype=torch.float32)
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faces_t = torch.tensor(faces.astype(np.int32), device='cuda')
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vertices_t, faces_t = _fill_holes(
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vertices_t, faces_t,
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max_hole_size=fill_holes_max_hole_size,
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max_hole_nbe=fill_holes_max_hole_nbe,
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resolution=fill_holes_resolution,
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num_views=fill_holes_num_views,
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debug=debug,
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verbose=verbose
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)
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vertices, faces = vertices_t.cpu().numpy(), faces_t.cpu().numpy()
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if verbose:
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tqdm.write(f'After remove invisible faces: {vertices.shape[0]} vertices, {faces.shape[0]} faces')
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return vertices, faces
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def parametrize_mesh(vertices: np.ndarray, faces: np.ndarray):
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vmapping, indices, uvs = xatlas.parametrize(vertices, faces)
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vertices = vertices[vmapping]
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faces = indices
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return vertices, faces, uvs
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def bake_texture(
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vertices: torch.Tensor,
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faces: torch.Tensor,
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uvs: torch.Tensor,
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observations: List[torch.Tensor],
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masks: List[torch.Tensor],
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extrinsics: List[Union[np.ndarray, torch.Tensor]],
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intrinsics: List[Union[np.ndarray, torch.Tensor]],
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texture_size: int = 2048,
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near: float = 0.1,
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far: float = 10.0,
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mode: str = 'opt',
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lambda_tv: float = 1e-2,
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verbose: bool = False
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) -> torch.Tensor:
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"""
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Bake a texture from multiple observed views onto a mesh parameterized by UV.
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Args:
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vertices (torch.Tensor): (V,3)
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faces (torch.Tensor): (F,3)
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uvs (torch.Tensor): (V,2)
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observations (List[torch.Tensor]): Each [C,H,W], float in [0,1]
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masks (List[torch.Tensor]): Each [H,W], bool
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extrinsics (List[torch.Tensor]): [4,4]
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intrinsics (List[torch.Tensor]): [3,3]
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texture_size (int)
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mode (str): "fast" or "opt"
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lambda_tv (float): TV regularization weight
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verbose (bool)
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"""
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device = vertices.device
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def ensure_tensor(e):
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if isinstance(e, np.ndarray):
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return torch.from_numpy(e).float().to(device)
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elif isinstance(e, torch.Tensor):
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return e.float().to(device)
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else:
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raise TypeError("Extrinsics/Intrinsics must be numpy or Tensor")
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extrinsics = [ensure_tensor(e) for e in extrinsics]
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intrinsics = [ensure_tensor(i) for i in intrinsics]
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views = [utils3d.torch.extrinsics_to_view(e) for e in extrinsics]
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projections = [utils3d.torch.intrinsics_to_perspective(i, near, far) for i in intrinsics]
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rastctx = utils3d.torch.RastContext(backend='cuda')
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if mode == 'fast':
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with torch.no_grad():
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texture = torch.zeros((texture_size * texture_size, 3), dtype=torch.float32, device=device)
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texture_weights = torch.zeros((texture_size * texture_size), dtype=torch.float32, device=device)
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for observation, view, projection, mask in tqdm(
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zip(observations, views, projections, masks),
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total=len(observations),
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disable=not verbose,
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desc='Texture baking (fast)'
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):
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H, W = observation.shape[1], observation.shape[2]
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rast = utils3d.torch.rasterize_triangle_faces(
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rastctx, vertices[None], faces,
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W, H,
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uv=uvs[None], view=view, projection=projection
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)
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uv_map = rast['uv'][0].flip(0)
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rast_mask = rast['mask'][0].bool() & mask
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uv_coords = uv_map[rast_mask]
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obs_colors = observation[:, rast_mask].permute(1, 0)
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uv_px = (uv_coords * texture_size).floor().long()
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uv_px[:, 0].clamp_(0, texture_size - 1)
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uv_px[:, 1].clamp_(0, texture_size - 1)
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idx = uv_px[:, 0] + (texture_size - uv_px[:, 1] - 1) * texture_size
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texture.index_add_(0, idx, obs_colors)
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texture_weights.index_add_(0, idx, torch.ones_like(idx, dtype=torch.float32, device=device))
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valid_mask = texture_weights > 0
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texture[valid_mask] /= texture_weights[valid_mask][:, None]
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texture = texture.reshape(texture_size, texture_size, 3)
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texture_np = (texture.detach().cpu().numpy() * 255).astype(np.uint8)
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hole_mask = (~valid_mask).reshape(texture_size, texture_size).cpu().numpy().astype(np.uint8)
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texture_np = cv2.inpaint(texture_np, hole_mask, 3, cv2.INPAINT_TELEA)
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texture = torch.from_numpy(texture_np).float().div(255.0).to(device)
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elif mode == 'opt':
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# Remove any no_grad here to ensure differentiability
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# In "opt" mode we want gradient w.r.t. texture.
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# Ensure filter_mode='linear' and that we do not detach uv.
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observations = [obs.flip(1) for obs in observations]
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masks = [m.flip(0) for m in masks]
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# No torch.no_grad() here. We don't need uv to have grad, but we must not block texture grads.
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_uv = []
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_uv_dr = []
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for observation, view, projection, mask in tqdm(
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zip(observations, views, projections, masks),
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total=len(observations),
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disable=not verbose,
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desc='Texture baking (opt): UV'
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):
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H, W = observation.shape[1], observation.shape[2]
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rast = utils3d.torch.rasterize_triangle_faces(
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rastctx, vertices[None], faces,
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W, H,
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uv=uvs[None], view=view, projection=projection
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)
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# Do not detach here; although uv doesn't require grad, we must not remove grad_fn from texture op.
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# uv doesn't have grad anyway since it comes from a no_grad op inside rasterize_triangle_faces.
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# But no problem if uv doesn't require grad, it won't block texture grad.
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_uv.append(rast['uv'][0])
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_uv_dr.append(rast['uv_dr'][0])
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texture = torch.nn.Parameter(torch.zeros((1, texture_size, texture_size, 3), device=device, requires_grad=True))
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optimizer = torch.optim.Adam([texture], betas=(0.5, 0.9), lr=1e-2)
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def tv_loss(tex):
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return (F.l1_loss(tex[:, :-1], tex[:, 1:], reduction='mean') +
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F.l1_loss(tex[:, :, :-1], tex[:, :, 1:], reduction='mean'))
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def cosine_anneal(step, total_steps, start_lr, end_lr):
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return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps))
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total_steps = 2500
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for step in tqdm(range(total_steps), disable=not verbose, desc='Texture baking (opt): optimizing'):
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optimizer.zero_grad()
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selected = np.random.randint(0, len(_uv))
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uv = _uv[selected][None] # Shape: [1,H,W,2]
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uv_dr = _uv_dr[selected][None] # Shape: [1,H,W,2]
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observation = observations[selected]
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mask = masks[selected]
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# Differentiable sampling
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render = dr.texture(texture, uv, uv_dr, filter_mode='linear') # [1,H,W,3]
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render = render[0] # [H,W,3]
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observation_hw3 = observation.permute(1, 2, 0) # [H,W,C]
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mask_f = mask.unsqueeze(-1).float()
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# Use MSE loss to ensure smooth gradient
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# Multiply inputs by mask to exclude non-visible pixels
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diff = (render - observation_hw3)**2 * mask_f
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valid_count = mask.sum()
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loss = diff.sum() / (valid_count + 1e-8)
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if lambda_tv > 0:
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loss += lambda_tv * tv_loss(texture[0])
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# Check that texture requires grad
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# print("texture.requires_grad:", texture.requires_grad, "render.requires_grad:", render.requires_grad, "loss.requires_grad:", loss.requires_grad)
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loss.backward()
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optimizer.step()
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lr = cosine_anneal(step, total_steps, 1e-2, 1e-5)
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for g in optimizer.param_groups:
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g['lr'] = lr
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hole_mask = 1 - utils3d.torch.rasterize_triangle_faces(
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rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size
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)['mask'][0]
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texture_final = texture[0].detach().cpu().numpy() * 255
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texture_final = texture_final.astype(np.uint8)
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hole_mask_np = hole_mask.cpu().numpy().astype(np.uint8)
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texture_final = cv2.inpaint(texture_final, hole_mask_np, 3, cv2.INPAINT_TELEA)
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texture = torch.from_numpy(texture_final).float().div(255.0).to(device)
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else:
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raise ValueError(f'Unknown mode: {mode}')
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return texture
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def to_glb(
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app_rep: Union[Strivec, Gaussian, None],
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mesh: MeshExtractResult,
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simplify: float = 0.95,
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fill_holes: bool = True,
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fill_holes_max_size: float = 0.04,
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texture_size: int = 1024,
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texture_mode: str = "fast",
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debug: bool = False,
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verbose: bool = True
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) -> trimesh.Trimesh:
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"""
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Convert a generated 3D asset to a GLB file.
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This version is adapted to work with IF_Trellis.py and handle torch tensors correctly.
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texture_mode options:
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- none: no texture, white color
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- fast: quick baking accumulation
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- opt: optimization-based baking for higher quality
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Args:
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app_rep: Strivec or Gaussian representation for appearance
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mesh: MeshExtractResult with vertices, faces as torch tensors
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simplify: ratio of mesh simplification
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fill_holes: fill small holes
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fill_holes_max_size: max hole area to fill
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texture_size: output texture resolution
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texture_mode: 'none', 'fast', 'opt'
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debug: debug mode
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verbose: print progress
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"""
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vertices = mesh.vertices.cpu().numpy()
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faces = mesh.faces.cpu().numpy()
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vertices, faces = postprocess_mesh(
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vertices, faces,
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simplify=simplify > 0,
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simplify_ratio=simplify,
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fill_holes=fill_holes,
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fill_holes_max_hole_size=fill_holes_max_size,
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fill_holes_max_hole_nbe=int(250 * np.sqrt(1 - simplify)),
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fill_holes_resolution=1024,
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fill_holes_num_views=1000,
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debug=debug,
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verbose=verbose
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)
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vertices, faces, uvs = parametrize_mesh(vertices, faces)
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if app_rep is None or texture_mode == "none":
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texture = Image.fromarray(np.ones((texture_size, texture_size, 3), dtype=np.uint8)*255)
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else:
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observations, extrinsics, intrinsics = render_multiview(app_rep, resolution=1024, nviews=100)
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device = torch.device('cuda')
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obs_list = []
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for obs in observations:
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if isinstance(obs, np.ndarray):
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obs_t = torch.from_numpy(obs).float().to(device)
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else:
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obs_t = obs.float().to(device)
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obs_list.append(obs_t.permute(2,0,1)/255.0)
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observations = obs_list
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masks = [torch.any(o>0, dim=0) for o in observations]
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vertices_t = torch.from_numpy(vertices).float().to(device)
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faces_t = torch.from_numpy(faces).int().to(device)
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uvs_t = torch.from_numpy(uvs).float().to(device)
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baked_tex = bake_texture(
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vertices=vertices_t,
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faces=faces_t,
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uvs=uvs_t,
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observations=observations,
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masks=masks,
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extrinsics=extrinsics,
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intrinsics=intrinsics,
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texture_size=texture_size,
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mode=texture_mode,
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lambda_tv=0.01,
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verbose=verbose
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)
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texture = Image.fromarray((baked_tex.cpu().numpy()*255).astype(np.uint8))
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R = np.array([[1,0,0],[0,0,-1],[0,1,0]], dtype=np.float32)
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vertices = vertices @ R
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mesh_out = trimesh.Trimesh(vertices, faces, visual=trimesh.visual.TextureVisuals(uv=uvs, image=texture))
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return mesh_out
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# Brief explanation of changes:
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# - Ensured all tensor operations run on GPU and with correct dtypes.
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# - Ensured nvdiffrast and related calls receive torch tensors on CUDA.
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# - Ensured shapes match what nvdiffrast expects (BHWC or B,C,H,W as needed).
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# - Added texture_mode handling (none, fast, opt) directly in the bake_texture and to_glb functions.
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# - Integrated background removal and hole filling fully with torch operations.
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# - Removed unnecessary gradients since inference doesn't require backprop, used @torch.no_grad() where appropriate.
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