# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
from difflib import unified_diff
import os
import numpy as np
import torch
from . import obj
from . import util
#########################################################################################
# Base mesh class
#
# Minibatch in mesh is supported, as long as each mesh shares the same edge connectivity.
#########################################################################################
class Mesh:
def __init__(self,
v_pos=None,
t_pos_idx=None,
v_nrm=None,
t_nrm_idx=None,
v_tex=None,
t_tex_idx=None,
v_tng=None,
t_tng_idx=None,
material=None,
base=None):
self.v_pos = v_pos
self.v_nrm = v_nrm
self.v_tex = v_tex
self.v_tng = v_tng
self.t_pos_idx = t_pos_idx
self.t_nrm_idx = t_nrm_idx
self.t_tex_idx = t_tex_idx
self.t_tng_idx = t_tng_idx
self.material = material
if base is not None:
self.copy_none(base)
def __len__(self):
return len(self.v_pos)
def copy_none(self, other):
if self.v_pos is None:
self.v_pos = other.v_pos
if self.t_pos_idx is None:
self.t_pos_idx = other.t_pos_idx
if self.v_nrm is None:
self.v_nrm = other.v_nrm
if self.t_nrm_idx is None:
self.t_nrm_idx = other.t_nrm_idx
if self.v_tex is None:
self.v_tex = other.v_tex
if self.t_tex_idx is None:
self.t_tex_idx = other.t_tex_idx
if self.v_tng is None:
self.v_tng = other.v_tng
if self.t_tng_idx is None:
self.t_tng_idx = other.t_tng_idx
if self.material is None:
self.material = other.material
def clone(self):
out = Mesh(base=self)
if out.v_pos is not None:
out.v_pos = out.v_pos.clone().detach()
if out.t_pos_idx is not None:
out.t_pos_idx = out.t_pos_idx.clone().detach()
if out.v_nrm is not None:
out.v_nrm = out.v_nrm.clone().detach()
if out.t_nrm_idx is not None:
out.t_nrm_idx = out.t_nrm_idx.clone().detach()
if out.v_tex is not None:
out.v_tex = out.v_tex.clone().detach()
if out.t_tex_idx is not None:
out.t_tex_idx = out.t_tex_idx.clone().detach()
if out.v_tng is not None:
out.v_tng = out.v_tng.clone().detach()
if out.t_tng_idx is not None:
out.t_tng_idx = out.t_tng_idx.clone().detach()
return out
def detach(self):
return self.clone()
def extend(self, N: int):
"""
Create new Mesh class which contains each input mesh N times.
Args:
N: number of new copies of each mesh.
Returns:
new Mesh object.
"""
verts = self.v_pos.repeat(N, 1, 1)
faces = self.t_pos_idx
uvs = self.v_tex.repeat(N, 1, 1)
uv_idx = self.t_tex_idx
mat = self.material
return make_mesh(verts, faces, uvs, uv_idx, self.material)
def deform(self, deformation):
"""
Create new Mesh class which is obtained by performing the deformation to the self.
Args:
deformation: tensor with shape (B, V, 3)
Returns:
new Mesh object after the deformation.
"""
assert deformation.shape[1] == self.v_pos.shape[1] and deformation.shape[2] == 3
verts = self.v_pos + deformation
return make_mesh(verts, self.t_pos_idx, self.v_tex.repeat(len(verts), 1, 1), self.t_tex_idx, self.material)
def get_m_to_n(self, m: int, n: int):
"""
Create new Mesh class with the n-th (included) mesh to the m-th (not included) mesh in the batch.
Args:
m: the index of the starting mesh to be contained.
n: the index of the first mesh not to be contained.
"""
verts = self.v_pos[m:n, ...]
faces = self.t_pos_idx
uvs = self.v_tex[m:n, ...]
uv_idx = self.t_tex_idx
mat = self.material
return make_mesh(verts, faces, uvs, uv_idx, mat)
def first_n(self, n: int):
"""
Create new Mesh class with only the first n meshes in the batch.
Args:
n: number of meshes to be contained.
Returns:
new Mesh object with the first n meshes.
"""
return self.get_m_to_n(0, n)
verts = self.v_pos[:n, ...]
faces = self.t_pos_idx
uvs = self.v_tex[:n, ...]
uv_idx = self.t_tex_idx
mat = self.material
return make_mesh(verts, faces, uvs, uv_idx, mat)
def get_n(self, n: int):
"""
Create new Mesh class with only the n-th meshes in the batch.
Args:
n: the index of the mesh to be contained.
Returns:
new Mesh object with the n-th mesh.
"""
verts = self.v_pos[n:n+1, ...]
faces = self.t_pos_idx
uvs = self.v_tex[n:n+1, ...]
uv_idx = self.t_tex_idx
mat = self.material
return make_mesh(verts, faces, uvs, uv_idx, mat)
######################################################################################
# Mesh loading helper
######################################################################################
def load_mesh(filename, mtl_override=None):
name, ext = os.path.splitext(filename)
if ext == ".obj":
return obj.load_obj(filename, clear_ks=True, mtl_override=mtl_override)
assert False, "Invalid mesh file extension"
######################################################################################
# Compute AABB
######################################################################################
def aabb(mesh):
return torch.min(mesh.v_pos, dim=0).values, torch.max(mesh.v_pos, dim=0).values
######################################################################################
# Compute unique edge list from attribute/vertex index list
######################################################################################
def compute_edges(attr_idx, return_inverse=False):
with torch.no_grad():
# Create all edges, packed by triangle
idx = attr_idx[0]
all_edges = torch.cat((
torch.stack((idx[:, 0], idx[:, 1]), dim=-1),
torch.stack((idx[:, 1], idx[:, 2]), dim=-1),
torch.stack((idx[:, 2], idx[:, 0]), dim=-1),
), dim=-1).view(-1, 2)
# Swap edge order so min index is always first
order = (all_edges[:, 0] > all_edges[:, 1]).long().unsqueeze(dim=1)
sorted_edges = torch.cat((
torch.gather(all_edges, 1, order),
torch.gather(all_edges, 1, 1 - order)
), dim=-1)
# Eliminate duplicates and return inverse mapping
return torch.unique(sorted_edges, dim=0, return_inverse=return_inverse)
######################################################################################
# Compute unique edge to face mapping from attribute/vertex index list
######################################################################################
def compute_edge_to_face_mapping(attr_idx, return_inverse=False):
with torch.no_grad():
# Get unique edges
# Create all edges, packed by triangle
idx = attr_idx[0]
all_edges = torch.cat((
torch.stack((idx[:, 0], idx[:, 1]), dim=-1),
torch.stack((idx[:, 1], idx[:, 2]), dim=-1),
torch.stack((idx[:, 2], idx[:, 0]), dim=-1),
), dim=-1).view(-1, 2)
# Swap edge order so min index is always first
order = (all_edges[:, 0] > all_edges[:, 1]).long().unsqueeze(dim=1)
sorted_edges = torch.cat((
torch.gather(all_edges, 1, order),
torch.gather(all_edges, 1, 1 - order)
), dim=-1)
# Elliminate duplicates and return inverse mapping
unique_edges, idx_map = torch.unique(sorted_edges, dim=0, return_inverse=True)
tris = torch.arange(idx.shape[0]).repeat_interleave(3).cuda()
tris_per_edge = torch.zeros((unique_edges.shape[0], 2), dtype=torch.int64).cuda()
# Compute edge to face table
mask0 = order[:,0] == 0
mask1 = order[:,0] == 1
tris_per_edge[idx_map[mask0], 0] = tris[mask0]
tris_per_edge[idx_map[mask1], 1] = tris[mask1]
return tris_per_edge
######################################################################################
# Align base mesh to reference mesh:move & rescale to match bounding boxes.
######################################################################################
def unit_size(mesh):
with torch.no_grad():
vmin, vmax = aabb(mesh)
scale = 2 / torch.max(vmax - vmin).item()
v_pos = mesh.v_pos - (vmax + vmin) / 2 # Center mesh on origin
v_pos = v_pos * scale # Rescale to unit size
return Mesh(v_pos, base=mesh)
######################################################################################
# Center & scale mesh for rendering
######################################################################################
def center_by_reference(base_mesh, ref_aabb, scale):
center = (ref_aabb[0] + ref_aabb[1]) * 0.5
scale = scale / torch.max(ref_aabb[1] - ref_aabb[0]).item()
v_pos = (base_mesh.v_pos - center[None, ...]) * scale
return Mesh(v_pos, base=base_mesh)
######################################################################################
# Simple smooth vertex normal computation
######################################################################################
def auto_normals(imesh):
batch_size = imesh.v_pos.shape[0]
i0 = imesh.t_pos_idx[0, :, 0] # Shape: (F)
i1 = imesh.t_pos_idx[0, :, 1] # Shape: (F)
i2 = imesh.t_pos_idx[0, :, 2] # Shape: (F)
v0 = imesh.v_pos[:, i0, :] # Shape: (B, F, 3)
v1 = imesh.v_pos[:, i1, :] # Shape: (B, F, 3)
v2 = imesh.v_pos[:, i2, :] # Shape: (B, F, 3)
face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1) # Shape: (B, F, 3)
# Splat face normals to vertices
v_nrm = torch.zeros_like(imesh.v_pos) # Shape: (B, V, 3)
v_nrm.scatter_add_(1, i0[None, :, None].repeat(batch_size, 1, 3), face_normals)
v_nrm.scatter_add_(1, i1[None, :, None].repeat(batch_size, 1, 3), face_normals)
v_nrm.scatter_add_(1, i2[None, :, None].repeat(batch_size, 1, 3), face_normals)
# Normalize, replace zero (degenerated) normals with some default value
v_nrm = torch.where(util.dot(v_nrm, v_nrm) > 1e-20,
v_nrm, torch.tensor([0.0, 0.0, 1.0],
dtype=torch.float32, device='cuda'))
v_nrm = util.safe_normalize(v_nrm)
if torch.is_anomaly_enabled():
assert torch.all(torch.isfinite(v_nrm))
return Mesh(v_nrm=v_nrm, t_nrm_idx=imesh.t_pos_idx, base=imesh)
######################################################################################
# Compute tangent space from texture map coordinates
# Follows http://www.mikktspace.com/ conventions
######################################################################################
def compute_tangents(imesh):
batch_size = imesh.v_pos.shape[0]
vn_idx = [None] * 3
pos = [None] * 3
tex = [None] * 3
for i in range(0,3):
pos[i] = imesh.v_pos[:, imesh.t_pos_idx[0, :, i]]
tex[i] = imesh.v_tex[:, imesh.t_tex_idx[0, :, i]]
vn_idx[i] = imesh.t_nrm_idx[..., i:i+1]
tangents = torch.zeros_like(imesh.v_nrm)
tansum = torch.zeros_like(imesh.v_nrm)
# Compute tangent space for each triangle
uve1 = tex[1] - tex[0] # Shape: (B, F, 2)
uve2 = tex[2] - tex[0] # Shape: (B, F, 2)
pe1 = pos[1] - pos[0] # Shape: (B, F, 3)
pe2 = pos[2] - pos[0] # Shape: (B, F, 3)
nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2] # Shape: (B, F, 3)
denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1] # Shape: (B, F, 1)
# Avoid division by zero for degenerated texture coordinates
tang = nom / torch.where(denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)) # Shape: (B, F, 3)
# Update all 3 vertices
for i in range(0,3):
idx = vn_idx[i].repeat(batch_size, 1, 3) # Shape: (B, F, 3)
tangents.scatter_add_(1, idx, tang) # tangents[n_i] = tangents[n_i] + tang
tansum.scatter_add_(1, idx, torch.ones_like(tang)) # tansum[n_i] = tansum[n_i] + 1
tangents = tangents / tansum
# Normalize and make sure tangent is perpendicular to normal
tangents = util.safe_normalize(tangents)
tangents = util.safe_normalize(tangents - util.dot(tangents, imesh.v_nrm) * imesh.v_nrm)
if torch.is_anomaly_enabled():
assert torch.all(torch.isfinite(tangents))
return Mesh(v_tng=tangents, t_tng_idx=imesh.t_nrm_idx, base=imesh)
######################################################################################
# Create new Mesh from verts, faces, uvs, and uv_idx. The rest is auto computed.
######################################################################################
def make_mesh(verts, faces, uvs, uv_idx, material):
"""
Create new Mesh class with given verts, faces, uvs, and uv_idx.
Args:
verts: tensor of shape (B, V, 3)
faces: tensor of shape (1, F, 3)
uvs: tensor of shape (B, V, 2)
uv_idx: tensor of shape (1, F, 3)
material: an Material instance, specifying the material of the mesh.
Returns:
new Mesh object.
"""
assert len(verts.shape) == 3 and len(faces.shape) == 3 and len(uvs.shape) == 3 and len(uv_idx.shape) == 3, "All components must be batched."
assert faces.shape[0] == 1 and uv_idx.shape[0] == 1, "Every mesh must share the same edge connectivity."
assert verts.shape[0] == uvs.shape[0], "Batch size must be consistent."
ret = Mesh(verts, faces, v_tex=uvs, t_tex_idx=uv_idx, material=material)
ret = auto_normals(ret)
ret = compute_tangents(ret)
return ret