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# 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 trimesh
import kaolin
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 Mesh:
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))
self.nrm = nrm
def load_mesh(path, device):
mesh_np = trimesh.load(path)
vertices = torch.tensor(mesh_np.vertices, device=device, dtype=torch.float)
faces = torch.tensor(mesh_np.faces, device=device, dtype=torch.long)
# Normalize
vmin, vmax = vertices.min(dim=0)[0], vertices.max(dim=0)[0]
scale = 1.8 / torch.max(vmax - vmin).item()
vertices = vertices - (vmax + vmin) / 2 # Center mesh on origin
vertices = vertices * scale # Rescale to [-0.9, 0.9]
return Mesh(vertices, faces)
def compute_sdf(points, vertices, faces):
face_vertices = kaolin.ops.mesh.index_vertices_by_faces(vertices.clone().unsqueeze(0), faces)
distance = kaolin.metrics.trianglemesh.point_to_mesh_distance(points.unsqueeze(0), face_vertices)[0]
with torch.no_grad():
sign = (kaolin.ops.mesh.check_sign(vertices.unsqueeze(0), faces, points.unsqueeze(0))<1).float() * 2 - 1
sdf = (sign*distance).squeeze(0)
return sdf
def sample_random_points(n, mesh):
pts_random = (torch.rand((n//2,3),device='cuda') - 0.5) * 2
pts_surface = kaolin.ops.mesh.sample_points(mesh.vertices.unsqueeze(0), mesh.faces, 500)[0].squeeze(0)
pts_surface += torch.randn_like(pts_surface) * 0.05
pts = torch.cat([pts_random, pts_surface])
return pts
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') |