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# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import numpy as np
import torch
import trimesh
def normalize_mesh(mesh, scale=0.9999):
"""
Normalize the mesh to fit inside a centered cube with a specified scale.
The mesh is translated so that its bounding box center is at the origin,
then uniformly scaled so that the longest side of the bounding box fits within [-scale, scale].
Args:
mesh (trimesh.Trimesh): Input mesh to normalize.
scale (float, optional): Scaling factor to slightly shrink the mesh inside the unit cube. Default is 0.9999.
Returns:
trimesh.Trimesh: The normalized mesh with applied translation and scaling.
"""
bbox = mesh.bounds
center = (bbox[1] + bbox[0]) / 2
scale_ = (bbox[1] - bbox[0]).max()
mesh.apply_translation(-center)
mesh.apply_scale(1 / scale_ * 2 * scale)
return mesh
def sample_pointcloud(mesh, num=200000):
"""
Sample points uniformly from the surface of the mesh along with their corresponding face normals.
Args:
mesh (trimesh.Trimesh): Input mesh to sample from.
num (int, optional): Number of points to sample. Default is 200000.
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- points: Sampled points as a float tensor of shape (num, 3).
- normals: Corresponding normals as a float tensor of shape (num, 3).
"""
points, face_idx = mesh.sample(num, return_index=True)
normals = mesh.face_normals[face_idx]
points = torch.from_numpy(points.astype(np.float32))
normals = torch.from_numpy(normals.astype(np.float32))
return points, normals
def load_surface(mesh, num_points=8192):
"""
Normalize the mesh, sample points and normals from its surface, and randomly select a subset.
Args:
mesh (trimesh.Trimesh): Input mesh to process.
num_points (int, optional): Number of points to randomly select
from the sampled surface points. Default is 8192.
Returns:
Tuple[torch.Tensor, trimesh.Trimesh]:
- surface: Tensor of shape (1, num_points, 6), concatenating points and normals.
- mesh: The normalized mesh.
"""
mesh = normalize_mesh(mesh, scale=0.98)
surface, normal = sample_pointcloud(mesh)
rng = np.random.default_rng(seed=0)
ind = rng.choice(surface.shape[0], num_points, replace=False)
surface = torch.FloatTensor(surface[ind])
normal = torch.FloatTensor(normal[ind])
surface = torch.cat([surface, normal], dim=-1).unsqueeze(0)
return surface, mesh
def sharp_sample_pointcloud(mesh, num=16384):
"""
Sample points and normals preferentially from sharp edges of the mesh.
Sharp edges are detected based on the angle between vertex normals and face normals.
Points are sampled along these edges proportionally to edge length.
Args:
mesh (trimesh.Trimesh): Input mesh to sample from.
num (int, optional): Number of points to sample from sharp edges. Default is 16384.
Returns:
Tuple[np.ndarray, np.ndarray]:
- samples: Sampled points along sharp edges, shape (num, 3).
- normals: Corresponding interpolated normals, shape (num, 3).
"""
V = mesh.vertices
N = mesh.face_normals
VN = mesh.vertex_normals
F = mesh.faces
VN2 = np.ones(V.shape[0])
for i in range(3):
dot = np.stack((VN2[F[:, i]], np.sum(VN[F[:, i]] * N, axis=-1)), axis=-1)
VN2[F[:, i]] = np.min(dot, axis=-1)
sharp_mask = VN2 < 0.985
# collect edge
edge_a = np.concatenate((F[:, 0], F[:, 1], F[:, 2]))
edge_b = np.concatenate((F[:, 1], F[:, 2], F[:, 0]))
sharp_edge = ((sharp_mask[edge_a] * sharp_mask[edge_b]))
edge_a = edge_a[sharp_edge > 0]
edge_b = edge_b[sharp_edge > 0]
sharp_verts_a = V[edge_a]
sharp_verts_b = V[edge_b]
sharp_verts_an = VN[edge_a]
sharp_verts_bn = VN[edge_b]
weights = np.linalg.norm(sharp_verts_b - sharp_verts_a, axis=-1)
weights /= np.sum(weights)
random_number = np.random.rand(num)
w = np.random.rand(num, 1)
index = np.searchsorted(weights.cumsum(), random_number)
samples = w * sharp_verts_a[index] + (1 - w) * sharp_verts_b[index]
normals = w * sharp_verts_an[index] + (1 - w) * sharp_verts_bn[index]
return samples, normals
def load_surface_sharpegde(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag=True):
try:
mesh_full = trimesh.util.concatenate(mesh.dump())
except Exception as err:
mesh_full = trimesh.util.concatenate(mesh)
mesh_full = normalize_mesh(mesh_full)
origin_num = mesh_full.faces.shape[0]
original_vertices = mesh_full.vertices
original_faces = mesh_full.faces
mesh = trimesh.Trimesh(vertices=original_vertices, faces=original_faces[:origin_num])
mesh_fill = trimesh.Trimesh(vertices=original_vertices, faces=original_faces[origin_num:])
area = mesh.area
area_fill = mesh_fill.area
sample_num = 499712 // 2
num_fill = int(sample_num * (area_fill / (area + area_fill)))
num = sample_num - num_fill
random_surface, random_normal = sample_pointcloud(mesh, num=num)
if num_fill == 0:
random_surface_fill, random_normal_fill = np.zeros((0, 3)), np.zeros((0, 3))
else:
random_surface_fill, random_normal_fill = sample_pointcloud(mesh_fill, num=num_fill)
random_sharp_surface, sharp_normal = sharp_sample_pointcloud(mesh, num=sample_num)
# save_surface
surface = np.concatenate((random_surface, random_normal), axis=1).astype(np.float16)
surface_fill = np.concatenate((random_surface_fill, random_normal_fill), axis=1).astype(np.float16)
sharp_surface = np.concatenate((random_sharp_surface, sharp_normal), axis=1).astype(np.float16)
surface = np.concatenate((surface, surface_fill), axis=0)
if sharpedge_flag:
sharpedge_label = np.zeros((surface.shape[0], 1))
surface = np.concatenate((surface, sharpedge_label), axis=1)
sharpedge_label = np.ones((sharp_surface.shape[0], 1))
sharp_surface = np.concatenate((sharp_surface, sharpedge_label), axis=1)
rng = np.random.default_rng()
ind = rng.choice(surface.shape[0], num_points, replace=False)
surface = torch.FloatTensor(surface[ind])
ind = rng.choice(sharp_surface.shape[0], num_sharp_points, replace=False)
sharp_surface = torch.FloatTensor(sharp_surface[ind])
return torch.cat([surface, sharp_surface], dim=0).unsqueeze(0), mesh_full
class SurfaceLoader:
def __init__(self, num_points=8192):
self.num_points = num_points
def __call__(self, mesh_or_mesh_path, num_points=None):
if num_points is None:
num_points = self.num_points
mesh = mesh_or_mesh_path
if isinstance(mesh, str):
mesh = trimesh.load(mesh, force="mesh", merge_primitives=True)
if isinstance(mesh, trimesh.scene.Scene):
for idx, obj in enumerate(mesh.geometry.values()):
if idx == 0:
temp_mesh = obj
else:
temp_mesh = temp_mesh + obj
mesh = temp_mesh
surface, mesh = load_surface(mesh, num_points=num_points)
return surface
class SharpEdgeSurfaceLoader:
def __init__(self, num_uniform_points=8192, num_sharp_points=8192, **kwargs):
self.num_uniform_points = num_uniform_points
self.num_sharp_points = num_sharp_points
self.num_points = num_uniform_points + num_sharp_points
def __call__(self, mesh_or_mesh_path, num_uniform_points=None, num_sharp_points=None):
if num_uniform_points is None:
num_uniform_points = self.num_uniform_points
if num_sharp_points is None:
num_sharp_points = self.num_sharp_points
mesh = mesh_or_mesh_path
if isinstance(mesh, str):
mesh = trimesh.load(mesh, force="mesh", merge_primitives=True)
if isinstance(mesh, trimesh.scene.Scene):
for idx, obj in enumerate(mesh.geometry.values()):
if idx == 0:
temp_mesh = obj
else:
temp_mesh = temp_mesh + obj
mesh = temp_mesh
surface, mesh = load_surface_sharpegde(mesh, num_points=num_uniform_points, num_sharp_points=num_sharp_points)
return surface
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