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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. 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
import numpy as np
# Initialize Warp with CUDA
wp.init()
@wp.kernel
def ray_triangle_intersection_kernel(
ray_origins: wp.array2d(dtype=wp.float32), # (H*W, 3)
ray_directions: wp.array2d(dtype=wp.float32), # (H*W, 3)
vertices: wp.array2d(dtype=wp.float32), # (N, 3)
faces: wp.array2d(dtype=wp.int32), # (M, 3)
depth_map: wp.array(dtype=wp.float32), # (H*W,)
num_triangles: wp.int32,
epsilon: wp.float32
):
"""
Warp kernel for ray-triangle intersection using Möller–Trumbore algorithm.
Each thread processes one ray against all triangles.
"""
# Get thread index (ray index)
ray_idx = wp.tid()
# Get ray origin and direction
ray_origin = wp.vec3(
ray_origins[ray_idx, 0],
ray_origins[ray_idx, 1],
ray_origins[ray_idx, 2]
)
ray_dir = wp.vec3(
ray_directions[ray_idx, 0],
ray_directions[ray_idx, 1],
ray_directions[ray_idx, 2]
)
# Initialize minimum distance
min_t = wp.float32(1e10)
# Iterate through all triangles
for tri_idx in range(num_triangles):
# Get triangle vertex indices
i0 = faces[tri_idx, 0]
i1 = faces[tri_idx, 1]
i2 = faces[tri_idx, 2]
# Get triangle vertices
v0 = wp.vec3(vertices[i0, 0], vertices[i0, 1], vertices[i0, 2])
v1 = wp.vec3(vertices[i1, 0], vertices[i1, 1], vertices[i1, 2])
v2 = wp.vec3(vertices[i2, 0], vertices[i2, 1], vertices[i2, 2])
# Compute edges
edge1 = v1 - v0
edge2 = v2 - v0
# Möller–Trumbore algorithm
h = wp.cross(ray_dir, edge2)
a = wp.dot(edge1, h)
# Check if ray is parallel to triangle
if wp.abs(a) < epsilon:
continue
f = 1.0 / a
s = ray_origin - v0
u = f * wp.dot(s, h)
# Check if intersection is within triangle (u >= 0 and u <= 1)
if u < 0.0 or u > 1.0:
continue
q = wp.cross(s, edge1)
v = f * wp.dot(ray_dir, q)
# Check if intersection is within triangle (v >= 0 and u + v <= 1)
if v < 0.0 or (u + v) > 1.0:
continue
# Compute t (distance along ray)
t = f * wp.dot(edge2, q)
# Only consider intersections in front of camera (t > 0)
if t > epsilon and t < min_t:
min_t = t
# Write result
if min_t < 1e10:
depth_map[ray_idx] = min_t
else:
depth_map[ray_idx] = 0.0
@wp.kernel
def ray_triangle_intersection_tiled_kernel(
ray_origins: wp.array2d(dtype=wp.float32), # (H*W, 3)
ray_directions: wp.array2d(dtype=wp.float32), # (H*W, 3)
vertices: wp.array2d(dtype=wp.float32), # (N, 3)
faces: wp.array2d(dtype=wp.int32), # (M, 3)
depth_map: wp.array(dtype=wp.float32), # (H*W,)
tri_start: wp.int32, # Start triangle index for this tile
tri_end: wp.int32, # End triangle index for this tile
epsilon: wp.float32
):
"""
Tiled version of ray-triangle intersection kernel.
Processes a subset of triangles to improve memory access patterns.
"""
# Get thread index (ray index)
ray_idx = wp.tid()
# Get ray origin and direction
ray_origin = wp.vec3(
ray_origins[ray_idx, 0],
ray_origins[ray_idx, 1],
ray_origins[ray_idx, 2]
)
ray_dir = wp.vec3(
ray_directions[ray_idx, 0],
ray_directions[ray_idx, 1],
ray_directions[ray_idx, 2]
)
# Get current minimum distance
min_t = depth_map[ray_idx]
if min_t == 0.0:
min_t = wp.float32(1e10)
# Process triangles in this tile
for tri_idx in range(tri_start, tri_end):
# Get triangle vertex indices
i0 = faces[tri_idx, 0]
i1 = faces[tri_idx, 1]
i2 = faces[tri_idx, 2]
# Get triangle vertices
v0 = wp.vec3(vertices[i0, 0], vertices[i0, 1], vertices[i0, 2])
v1 = wp.vec3(vertices[i1, 0], vertices[i1, 1], vertices[i1, 2])
v2 = wp.vec3(vertices[i2, 0], vertices[i2, 1], vertices[i2, 2])
# Compute edges
edge1 = v1 - v0
edge2 = v2 - v0
# Möller–Trumbore algorithm
h = wp.cross(ray_dir, edge2)
a = wp.dot(edge1, h)
# Check if ray is parallel to triangle
if wp.abs(a) < epsilon:
continue
f = 1.0 / a
s = ray_origin - v0
u = f * wp.dot(s, h)
# Check if intersection is within triangle (u >= 0 and u <= 1)
if u < 0.0 or u > 1.0:
continue
q = wp.cross(s, edge1)
v = f * wp.dot(ray_dir, q)
# Check if intersection is within triangle (v >= 0 and u + v <= 1)
if v < 0.0 or (u + v) > 1.0:
continue
# Compute t (distance along ray)
t = f * wp.dot(edge2, q)
# Only consider intersections in front of camera (t > 0)
if t > epsilon and t < min_t:
min_t = t
# Write result using atomic min to handle concurrent updates
if min_t < 1e10:
wp.atomic_min(depth_map, ray_idx, min_t)
def ray_triangle_intersection_warp(
ray_origins: torch.Tensor, # (H, W, 3)
ray_directions: torch.Tensor, # (H, W, 3)
vertices: torch.Tensor, # (N, 3)
faces: torch.Tensor, # (M, 3)
device: torch.device
) -> torch.Tensor:
"""
Compute ray-triangle intersections using NVIDIA Warp for maximum GPU acceleration.
This implementation uses Warp kernels to achieve the best possible performance
on NVIDIA GPUs by:
1. Using native CUDA kernels through Warp
2. Tiling triangles for better memory access patterns
3. Using atomic operations for concurrent updates
4. Minimizing memory transfers
Args:
ray_origins: (H, W, 3) ray origins in camera space
ray_directions: (H, W, 3) ray directions (should be normalized)
vertices: (N, 3) mesh vertices
faces: (M, 3) triangle face indices
device: torch device (must be CUDA)
Returns:
depth_map: (H, W) depth values, 0 where no intersection
"""
H, W = ray_origins.shape[:2]
num_rays = H * W
num_triangles = faces.shape[0]
# Reshape rays to 2D arrays
ray_origins_flat = ray_origins.reshape(-1, 3).contiguous()
ray_directions_flat = ray_directions.reshape(-1, 3).contiguous()
# Convert PyTorch tensors to Warp arrays (as float arrays, not vec3)
wp_ray_origins = wp.from_torch(ray_origins_flat, dtype=wp.float32)
wp_ray_directions = wp.from_torch(ray_directions_flat, dtype=wp.float32)
wp_vertices = wp.from_torch(vertices.contiguous(), dtype=wp.float32)
wp_faces = wp.from_torch(faces.int().contiguous(), dtype=wp.int32)
# Create output depth map
depth_map_flat = torch.zeros(num_rays, device=device, dtype=torch.float32)
wp_depth_map = wp.from_torch(depth_map_flat, dtype=wp.float32)
# Choose implementation based on problem size
if num_triangles < 10000:
# For smaller meshes, use simple kernel
wp.launch(
kernel=ray_triangle_intersection_kernel,
dim=num_rays,
inputs=[
wp_ray_origins,
wp_ray_directions,
wp_vertices,
wp_faces,
wp_depth_map,
num_triangles,
1e-8 # epsilon
],
device=f"cuda:{device.index}" if device.index is not None else "cuda:0"
)
else:
# For larger meshes, use tiled approach for better memory access
triangle_tile_size = 10000 # Process triangles in tiles
# Initialize depth map to infinity
depth_map_flat.fill_(float('inf'))
# Process triangles in tiles
for tri_start in range(0, num_triangles, triangle_tile_size):
tri_end = min(tri_start + triangle_tile_size, num_triangles)
wp.launch(
kernel=ray_triangle_intersection_tiled_kernel,
dim=num_rays,
inputs=[
wp_ray_origins,
wp_ray_directions,
wp_vertices,
wp_faces,
wp_depth_map,
tri_start,
tri_end,
1e-8 # epsilon
],
device=f"cuda:{device.index}" if device.index is not None else "cuda:0"
)
# Convert infinity back to 0
depth_map_flat[depth_map_flat == float('inf')] = 0.0
# Synchronize to ensure kernel completion
wp.synchronize()
# Reshape back to 2D
depth_map = depth_map_flat.reshape(H, W)
return depth_map
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