560 lines
19 KiB
Python
560 lines
19 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import craftsman
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from craftsman.utils.typing import *
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def dot(x, y):
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return torch.sum(x * y, -1, keepdim=True)
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class Mesh:
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def __init__(
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self, v_pos: Float[Tensor, "Nv 3"], t_pos_idx: Integer[Tensor, "Nf 3"], **kwargs
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) -> None:
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self.v_pos: Float[Tensor, "Nv 3"] = v_pos
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self.t_pos_idx: Integer[Tensor, "Nf 3"] = t_pos_idx
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self._v_nrm: Optional[Float[Tensor, "Nv 3"]] = None
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self._v_tng: Optional[Float[Tensor, "Nv 3"]] = None
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self._v_tex: Optional[Float[Tensor, "Nt 3"]] = None
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self._t_tex_idx: Optional[Float[Tensor, "Nf 3"]] = None
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self._v_rgb: Optional[Float[Tensor, "Nv 3"]] = None
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self._edges: Optional[Integer[Tensor, "Ne 2"]] = None
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self.extras: Dict[str, Any] = {}
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for k, v in kwargs.items():
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self.add_extra(k, v)
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def add_extra(self, k, v):
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self.extras[k] = v
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def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]):
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if self.requires_grad:
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craftsman.debug("Mesh is differentiable, not removing outliers")
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return self
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# use trimesh to first split the mesh into connected components
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# then remove the components with less than n_face_threshold faces
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import trimesh
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# construct a trimesh object
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mesh = trimesh.Trimesh(
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vertices=self.v_pos.detach().cpu().numpy(),
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faces=self.t_pos_idx.detach().cpu().numpy(),
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)
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# split the mesh into connected components
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components = mesh.split(only_watertight=False)
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# log the number of faces in each component
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craftsman.debug(
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"Mesh has {} components, with faces: {}".format(
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len(components), [c.faces.shape[0] for c in components]
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)
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)
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n_faces_threshold: int
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if isinstance(outlier_n_faces_threshold, float):
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# set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold
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n_faces_threshold = int(
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max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold
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)
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else:
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# set the threshold directly to outlier_n_faces_threshold
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n_faces_threshold = outlier_n_faces_threshold
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# log the threshold
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craftsman.debug(
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"Removing components with less than {} faces".format(n_faces_threshold)
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)
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# remove the components with less than n_face_threshold faces
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components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]
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# log the number of faces in each component after removing outliers
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craftsman.debug(
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"Mesh has {} components after removing outliers, with faces: {}".format(
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len(components), [c.faces.shape[0] for c in components]
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)
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)
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# merge the components
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mesh = trimesh.util.concatenate(components)
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# convert back to our mesh format
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v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)
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t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)
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clean_mesh = Mesh(v_pos, t_pos_idx)
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# keep the extras unchanged
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if len(self.extras) > 0:
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clean_mesh.extras = self.extras
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craftsman.debug(
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f"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}"
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)
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return clean_mesh
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@property
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def requires_grad(self):
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return self.v_pos.requires_grad
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@property
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def v_nrm(self):
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if self._v_nrm is None:
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self._v_nrm = self._compute_vertex_normal()
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return self._v_nrm
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@property
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def v_tng(self):
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if self._v_tng is None:
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self._v_tng = self._compute_vertex_tangent()
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return self._v_tng
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@property
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def v_tex(self):
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if self._v_tex is None:
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self._v_tex, self._t_tex_idx = self._unwrap_uv()
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return self._v_tex
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@property
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def t_tex_idx(self):
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if self._t_tex_idx is None:
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self._v_tex, self._t_tex_idx = self._unwrap_uv()
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return self._t_tex_idx
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@property
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def v_rgb(self):
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return self._v_rgb
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@property
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def edges(self):
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if self._edges is None:
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self._edges = self._compute_edges()
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return self._edges
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def _compute_vertex_normal(self):
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i0 = self.t_pos_idx[:, 0]
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i1 = self.t_pos_idx[:, 1]
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i2 = self.t_pos_idx[:, 2]
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v0 = self.v_pos[i0, :]
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v1 = self.v_pos[i1, :]
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v2 = self.v_pos[i2, :]
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face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
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# Splat face normals to vertices
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v_nrm = torch.zeros_like(self.v_pos)
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v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
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v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
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v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
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# Normalize, replace zero (degenerated) normals with some default value
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v_nrm = torch.where(
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dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
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)
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v_nrm = F.normalize(v_nrm, dim=1)
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if torch.is_anomaly_enabled():
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assert torch.all(torch.isfinite(v_nrm))
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return v_nrm
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def _compute_vertex_tangent(self):
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vn_idx = [None] * 3
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pos = [None] * 3
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tex = [None] * 3
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for i in range(0, 3):
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pos[i] = self.v_pos[self.t_pos_idx[:, i]]
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tex[i] = self.v_tex[self.t_tex_idx[:, i]]
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# t_nrm_idx is always the same as t_pos_idx
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vn_idx[i] = self.t_pos_idx[:, i]
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tangents = torch.zeros_like(self.v_nrm)
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tansum = torch.zeros_like(self.v_nrm)
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# Compute tangent space for each triangle
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uve1 = tex[1] - tex[0]
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uve2 = tex[2] - tex[0]
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pe1 = pos[1] - pos[0]
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pe2 = pos[2] - pos[0]
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nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]
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denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]
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# Avoid division by zero for degenerated texture coordinates
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tang = nom / torch.where(
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denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)
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)
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# Update all 3 vertices
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for i in range(0, 3):
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idx = vn_idx[i][:, None].repeat(1, 3)
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tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang
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tansum.scatter_add_(
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0, idx, torch.ones_like(tang)
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) # tansum[n_i] = tansum[n_i] + 1
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tangents = tangents / tansum
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# Normalize and make sure tangent is perpendicular to normal
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tangents = F.normalize(tangents, dim=1)
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tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)
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if torch.is_anomaly_enabled():
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assert torch.all(torch.isfinite(tangents))
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return tangents
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def _unwrap_uv(
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self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
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):
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craftsman.info("Using xatlas to perform UV unwrapping, may take a while ...")
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import xatlas
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atlas = xatlas.Atlas()
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atlas.add_mesh(
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self.v_pos.detach().cpu().numpy(),
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self.t_pos_idx.cpu().numpy(),
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)
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co = xatlas.ChartOptions()
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po = xatlas.PackOptions()
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for k, v in xatlas_chart_options.items():
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setattr(co, k, v)
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for k, v in xatlas_pack_options.items():
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setattr(po, k, v)
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setattr(co, 'max_cost', 2.0)
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setattr(po, 'resolution', 4096)
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atlas.generate(co, po, verbose=True)
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vmapping, indices, uvs = atlas.get_mesh(0)
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vmapping = (
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torch.from_numpy(
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vmapping.astype(np.uint64, casting="same_kind").view(np.int64)
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)
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.to(self.v_pos.device)
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.long()
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)
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uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()
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indices = (
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torch.from_numpy(
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indices.astype(np.uint64, casting="same_kind").view(np.int64)
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)
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.to(self.v_pos.device)
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.long()
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)
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return uvs, indices
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def unwrap_uv(
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self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
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):
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self._v_tex, self._t_tex_idx = self._unwrap_uv(
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xatlas_chart_options, xatlas_pack_options
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)
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def set_vertex_color(self, v_rgb):
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assert v_rgb.shape[0] == self.v_pos.shape[0]
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self._v_rgb = v_rgb
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def _compute_edges(self):
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# Compute edges
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edges = torch.cat(
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[
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self.t_pos_idx[:, [0, 1]],
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self.t_pos_idx[:, [1, 2]],
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self.t_pos_idx[:, [2, 0]],
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],
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dim=0,
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)
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edges = edges.sort()[0]
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edges = torch.unique(edges, dim=0)
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return edges
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def normal_consistency(self) -> Float[Tensor, ""]:
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edge_nrm: Float[Tensor, "Ne 2 3"] = self.v_nrm[self.edges]
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nc = (
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1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)
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).mean()
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return nc
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def _laplacian_uniform(self):
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# from stable-dreamfusion
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# https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224
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verts, faces = self.v_pos, self.t_pos_idx
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V = verts.shape[0]
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F = faces.shape[0]
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# Neighbor indices
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ii = faces[:, [1, 2, 0]].flatten()
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jj = faces[:, [2, 0, 1]].flatten()
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adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(
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dim=1
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)
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adj_values = torch.ones(adj.shape[1]).to(verts)
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# Diagonal indices
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diag_idx = adj[0]
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# Build the sparse matrix
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idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)
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values = torch.cat((-adj_values, adj_values))
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# The coalesce operation sums the duplicate indices, resulting in the
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# correct diagonal
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return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()
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def laplacian(self) -> Float[Tensor, ""]:
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with torch.no_grad():
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L = self._laplacian_uniform()
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loss = L.mm(self.v_pos)
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loss = loss.norm(dim=1)
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loss = loss.mean()
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return loss
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class IsosurfaceHelper(nn.Module):
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points_range: Tuple[float, float] = (0, 1)
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@property
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def grid_vertices(self) -> Float[Tensor, "N 3"]:
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raise NotImplementedError
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class MarchingCubeCPUHelper(IsosurfaceHelper):
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def __init__(self, resolution: int) -> None:
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super().__init__()
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self.resolution = resolution
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import mcubes
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self.mc_func: Callable = mcubes.marching_cubes
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self._grid_vertices: Optional[Float[Tensor, "N3 3"]] = None
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self._dummy: Float[Tensor, "..."]
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self.register_buffer(
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"_dummy", torch.zeros(0, dtype=torch.float32), persistent=False
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)
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@property
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def grid_vertices(self) -> Float[Tensor, "N3 3"]:
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if self._grid_vertices is None:
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# keep the vertices on CPU so that we can support very large resolution
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x, y, z = (
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torch.linspace(*self.points_range, self.resolution),
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torch.linspace(*self.points_range, self.resolution),
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torch.linspace(*self.points_range, self.resolution),
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)
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x, y, z = torch.meshgrid(x, y, z, indexing="ij")
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verts = torch.cat(
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[x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1
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).reshape(-1, 3)
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self._grid_vertices = verts
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return self._grid_vertices
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def forward(
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self,
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level: Float[Tensor, "N3 1"],
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deformation: Optional[Float[Tensor, "N3 3"]] = None,
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) -> Mesh:
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if deformation is not None:
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craftsman.warn(
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f"{self.__class__.__name__} does not support deformation. Ignoring."
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)
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level = -level.view(self.resolution, self.resolution, self.resolution)
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v_pos, t_pos_idx = self.mc_func(
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level.detach().cpu().numpy(), 0.0
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) # transform to numpy
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v_pos, t_pos_idx = (
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torch.from_numpy(v_pos).float().to(self._dummy.device),
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torch.from_numpy(t_pos_idx.astype(np.int64)).long().to(self._dummy.device),
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) # transform back to torch tensor on CUDA
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v_pos = v_pos / (self.resolution - 1.0)
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return Mesh(v_pos=v_pos, t_pos_idx=t_pos_idx)
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class MarchingTetrahedraHelper(IsosurfaceHelper):
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def __init__(self, resolution: int, tets_path: str):
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super().__init__()
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self.resolution = resolution
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self.tets_path = tets_path
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self.triangle_table: Float[Tensor, "..."]
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self.register_buffer(
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"triangle_table",
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torch.as_tensor(
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[
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[-1, -1, -1, -1, -1, -1],
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[1, 0, 2, -1, -1, -1],
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[4, 0, 3, -1, -1, -1],
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[1, 4, 2, 1, 3, 4],
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[3, 1, 5, -1, -1, -1],
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[2, 3, 0, 2, 5, 3],
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[1, 4, 0, 1, 5, 4],
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[4, 2, 5, -1, -1, -1],
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[4, 5, 2, -1, -1, -1],
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[4, 1, 0, 4, 5, 1],
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[3, 2, 0, 3, 5, 2],
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[1, 3, 5, -1, -1, -1],
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[4, 1, 2, 4, 3, 1],
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[3, 0, 4, -1, -1, -1],
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[2, 0, 1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1],
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],
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dtype=torch.long,
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),
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persistent=False,
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)
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self.num_triangles_table: Integer[Tensor, "..."]
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self.register_buffer(
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"num_triangles_table",
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torch.as_tensor(
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[0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long
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),
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persistent=False,
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)
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self.base_tet_edges: Integer[Tensor, "..."]
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self.register_buffer(
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"base_tet_edges",
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torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),
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persistent=False,
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)
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tets = np.load(self.tets_path)
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self._grid_vertices: Float[Tensor, "..."]
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self.register_buffer(
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"_grid_vertices",
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torch.from_numpy(tets["vertices"]).float(),
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persistent=False,
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)
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self.indices: Integer[Tensor, "..."]
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self.register_buffer(
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"indices", torch.from_numpy(tets["indices"]).long(), persistent=False
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)
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self._all_edges: Optional[Integer[Tensor, "Ne 2"]] = None
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def normalize_grid_deformation(
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self, grid_vertex_offsets: Float[Tensor, "Nv 3"]
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) -> Float[Tensor, "Nv 3"]:
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return (
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(self.points_range[1] - self.points_range[0])
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/ (self.resolution) # half tet size is approximately 1 / self.resolution
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* torch.tanh(grid_vertex_offsets)
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) # FIXME: hard-coded activation
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@property
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def grid_vertices(self) -> Float[Tensor, "Nv 3"]:
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return self._grid_vertices
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@property
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def all_edges(self) -> Integer[Tensor, "Ne 2"]:
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if self._all_edges is None:
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# compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)
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edges = torch.tensor(
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[0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],
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dtype=torch.long,
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device=self.indices.device,
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)
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_all_edges = self.indices[:, edges].reshape(-1, 2)
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_all_edges_sorted = torch.sort(_all_edges, dim=1)[0]
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_all_edges = torch.unique(_all_edges_sorted, dim=0)
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self._all_edges = _all_edges
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return self._all_edges
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def sort_edges(self, edges_ex2):
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with torch.no_grad():
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order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()
|
|
order = order.unsqueeze(dim=1)
|
|
|
|
a = torch.gather(input=edges_ex2, index=order, dim=1)
|
|
b = torch.gather(input=edges_ex2, index=1 - order, dim=1)
|
|
|
|
return torch.stack([a, b], -1)
|
|
|
|
def _forward(self, pos_nx3, sdf_n, tet_fx4):
|
|
with torch.no_grad():
|
|
occ_n = sdf_n > 0
|
|
occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
|
|
occ_sum = torch.sum(occ_fx4, -1)
|
|
valid_tets = (occ_sum > 0) & (occ_sum < 4)
|
|
occ_sum = occ_sum[valid_tets]
|
|
|
|
# find all vertices
|
|
all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)
|
|
all_edges = self.sort_edges(all_edges)
|
|
unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
|
|
|
|
unique_edges = unique_edges.long()
|
|
mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
|
|
mapping = (
|
|
torch.ones(
|
|
(unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device
|
|
)
|
|
* -1
|
|
)
|
|
mapping[mask_edges] = torch.arange(
|
|
mask_edges.sum(), dtype=torch.long, device=pos_nx3.device
|
|
)
|
|
idx_map = mapping[idx_map] # map edges to verts
|
|
|
|
interp_v = unique_edges[mask_edges]
|
|
edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
|
|
edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
|
|
edges_to_interp_sdf[:, -1] *= -1
|
|
|
|
denominator = edges_to_interp_sdf.sum(1, keepdim=True)
|
|
|
|
edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
|
|
verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
|
|
|
|
idx_map = idx_map.reshape(-1, 6)
|
|
|
|
v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))
|
|
tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
|
|
num_triangles = self.num_triangles_table[tetindex]
|
|
|
|
# Generate triangle indices
|
|
faces = torch.cat(
|
|
(
|
|
torch.gather(
|
|
input=idx_map[num_triangles == 1],
|
|
dim=1,
|
|
index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],
|
|
).reshape(-1, 3),
|
|
torch.gather(
|
|
input=idx_map[num_triangles == 2],
|
|
dim=1,
|
|
index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],
|
|
).reshape(-1, 3),
|
|
),
|
|
dim=0,
|
|
)
|
|
|
|
return verts, faces
|
|
|
|
def forward(
|
|
self,
|
|
level: Float[Tensor, "N3 1"],
|
|
deformation: Optional[Float[Tensor, "N3 3"]] = None,
|
|
) -> Mesh:
|
|
if deformation is not None:
|
|
grid_vertices = self.grid_vertices + self.normalize_grid_deformation(
|
|
deformation
|
|
)
|
|
else:
|
|
grid_vertices = self.grid_vertices
|
|
|
|
v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)
|
|
|
|
mesh = Mesh(
|
|
v_pos=v_pos,
|
|
t_pos_idx=t_pos_idx,
|
|
# extras
|
|
grid_vertices=grid_vertices,
|
|
tet_edges=self.all_edges,
|
|
grid_level=level,
|
|
grid_deformation=deformation,
|
|
)
|
|
|
|
return mesh
|