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c3d0544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | # SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import warp as wp
wp.config.quiet = True
@wp.kernel
def _bvh_query_distance(
mesh_id: wp.uint64,
points: wp.array(dtype=wp.vec3f),
max_dist: wp.float32,
sdf: wp.array(dtype=wp.float32),
sdf_hit_point: wp.array(dtype=wp.vec3f),
use_sign_winding_number: bool = False,
):
"""
Computes the signed distance from each point in the given array `points`
to the mesh represented by `mesh`,within the maximum distance `max_dist`,
and stores the result in the array `sdf`.
Parameters:
mesh (wp.uint64): The identifier of the mesh.
points (wp.array): An array of 3D points for which to compute the
signed distance.
max_dist (wp.float32): The maximum distance within which to search
for the closest point on the mesh.
sdf (wp.array): An array to store the computed signed distances.
sdf_hit_point (wp.array): An array to store the computed hit points.
sdf_hit_point_id (wp.array): An array to store the computed hit point ids.
use_sign_winding_number (bool): Flag to use sign_winding_number method for SDF.
Returns:
None
"""
tid = wp.tid()
if use_sign_winding_number:
res = wp.mesh_query_point_sign_winding_number(mesh_id, points[tid], max_dist)
else:
res = wp.mesh_query_point_sign_normal(mesh_id, points[tid], max_dist)
mesh = wp.mesh_get(mesh_id)
p0 = mesh.points[mesh.indices[3 * res.face + 0]]
p1 = mesh.points[mesh.indices[3 * res.face + 1]]
p2 = mesh.points[mesh.indices[3 * res.face + 2]]
p_closest = res.u * p0 + res.v * p1 + (1.0 - res.u - res.v) * p2
sdf[tid] = res.sign * wp.abs(wp.length(points[tid] - p_closest))
sdf_hit_point[tid] = p_closest
@torch.library.custom_op("physicsnemo::signed_distance_field", mutates_args=())
def signed_distance_field(
mesh_vertices: torch.Tensor,
mesh_indices: torch.Tensor,
input_points: torch.Tensor,
max_dist: float = 1e8,
use_sign_winding_number: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Computes the signed distance field (SDF) for a given mesh and input points.
The mesh must be a surface mesh consisting of all triangles. Uses NVIDIA
Warp for GPU acceleration.
Parameters:
----------
mesh_vertices (np.ndarray): Coordinates of the vertices of the mesh;
shape: (n_vertices, 3)
mesh_indices (np.ndarray): Indices corresponding to the faces of the
mesh; shape: (n_faces, 3)
input_points (np.ndarray): Coordinates of the points for which to
compute the SDF; shape: (n_points, 3)
max_dist (float, optional): Maximum distance within which
to search for the closest point on the mesh. Default is 1e8.
include_hit_points (bool, optional): Whether to include hit points in
the output. Here,
use_sign_winding_number (bool, optional): Whether to use sign winding
number method for SDF. Default is False. If False, your mesh should
be watertight to obtain correct results.
return_cupy (bool, optional): Whether to return a CuPy array. Default is
None, which means the function will automatically determine the
appropriate return type based on the input types.
Returns:
-------
Returns:
tuple[torch.Tensor, torch.Tensor] of:
- signed distance to the mesh, per input point
- hit point, per input point. "hit points" are the points on the
mesh that are closest to the input points, and hence, are
defining the SDF.
Example:
-------
>>> mesh_vertices = [(0, 0, 0), (1, 0, 0), (0, 1, 0)]
>>> mesh_indices = torch.tensor((0, 1, 2))
>>> input_points = torch.tensor((0.5, 0.5, 0.5))
>>> signed_distance_field(mesh_vertices, mesh_indices, input_points)
(tensor([0.5]), tensor([0.5, 0.5, 0.5]))
"""
if input_points.shape[-1] != 3:
raise ValueError("Input points must be a tensor with last dimension of size 3")
input_shape = input_points.shape
# Flatten the input points:
input_points = input_points.reshape(-1, 3)
N = len(input_points)
# Allocate output tensors with torch:
sdf = torch.zeros(N, dtype=torch.float32, device=input_points.device)
sdf_hit_point = torch.zeros(N, 3, dtype=torch.float32, device=input_points.device)
if input_points.device.type == "cuda":
wp_launch_stream = wp.stream_from_torch(
torch.cuda.current_stream(input_points.device)
)
wp_launch_device = None # We explicitly pass None if using the stream.
else:
wp_launch_stream = None
wp_launch_device = "cpu" # CPUs have no streams
with wp.ScopedStream(wp_launch_stream):
wp.init()
# zero copy the vertices, indices, and input points to warp:
wp_vertices = wp.from_torch(mesh_vertices.to(torch.float32), dtype=wp.vec3)
wp_indices = wp.from_torch(mesh_indices.to(torch.int32), dtype=wp.int32)
wp_input_points = wp.from_torch(input_points.to(torch.float32), dtype=wp.vec3)
# Convert output points:
wp_sdf = wp.from_torch(sdf, dtype=wp.float32)
wp_sdf_hit_point = wp.from_torch(sdf_hit_point, dtype=wp.vec3f)
mesh = wp.Mesh(
points=wp_vertices,
indices=wp_indices,
support_winding_number=use_sign_winding_number,
)
wp.launch(
kernel=_bvh_query_distance,
dim=N,
inputs=[
mesh.id,
wp_input_points,
max_dist,
wp_sdf,
wp_sdf_hit_point,
use_sign_winding_number,
],
device=wp_launch_device,
stream=wp_launch_stream,
)
# Unflatten the output to be like the input:
sdf = sdf.reshape(input_shape[:-1])
sdf_hit_point = sdf_hit_point.reshape(input_shape)
return sdf.to(input_points.dtype), sdf_hit_point.to(input_points.dtype)
@signed_distance_field.register_fake
def _(
mesh_vertices: torch.Tensor,
mesh_indices: torch.Tensor,
input_points: torch.Tensor,
max_dist: float = 1e8,
use_sign_winding_number: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
if mesh_vertices.device != input_points.device:
raise RuntimeError("mesh_vertices and input_points must be on the same device")
if mesh_vertices.device != mesh_indices.device:
raise RuntimeError("mesh_vertices and mesh_indices must be on the same device")
N = input_points.shape[0]
sdf_output = torch.empty(N, 1, device=input_points.device, dtype=input_points.dtype)
sdf_hit_point_output = torch.empty(
N, 3, device=input_points.device, dtype=input_points.dtype
)
return sdf_output, sdf_hit_point_output
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