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https://github.com/dendenxu/bvh-ray-tracing
CUDA Ray Tracing using BVH. Forked and modified from https://github.com/YuliangXiu/bvh-distance-queries
https://github.com/dendenxu/bvh-ray-tracing
bvh cuda pytorch ray-tracing ray-triangle-intersection
Last synced: 11 days ago
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CUDA Ray Tracing using BVH. Forked and modified from https://github.com/YuliangXiu/bvh-distance-queries
- Host: GitHub
- URL: https://github.com/dendenxu/bvh-ray-tracing
- Owner: dendenxu
- License: other
- Created: 2023-01-18T04:41:56.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-01-05T10:28:16.000Z (about 1 year ago)
- Last Synced: 2024-11-28T15:19:25.707Z (2 months ago)
- Topics: bvh, cuda, pytorch, ray-tracing, ray-triangle-intersection
- Language: C
- Homepage:
- Size: 1.46 MB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CUDA Ray Tracing using BVH
**[WIP] Supporting Ray-Triangle Intersection -> Ray Tracing on CUDA.**
This is a fork from: https://github.com/YuliangXiu/bvh-distance-queries
This package provides a PyTorch module that performs ray to surface queries on the GPU
**Please ignore the readme below for now.**
## Table of Contents
- [CUDA Ray Tracing using BVH](#cuda-ray-tracing-using-bvh)
- [Table of Contents](#table-of-contents)
- [License](#license)
- [Description](#description)
- [Installation](#installation)
- [Examples](#examples)
- [Dependencies](#dependencies)
- [Example dependencies](#example-dependencies)
- [Running on Cluster](#running-on-cluster)
- [Citation](#citation)
- [Contact](#contact)
## License
Software Copyright License for **non-commercial scientific research purposes**.
By downloading and/or using the Model & Software (including downloading, cloning,
installing, and any other use of this github repository), you acknowledge that
you have read these terms and conditions, understand them, and agree to be bound
by them. If you do not agree with these terms and conditions, you must not
download and/or use the Model & Software. Any infringement of the terms of this
agreement will automatically terminate your rights under this
[License](./LICENSE).
## Description
This repository provides a PyTorch wrapper around a CUDA kernel that implements
the method described in [Maximizing parallelism in the construction of BVHs,
octrees, and k-d trees](https://dl.acm.org/citation.cfm?id=2383801). More
specifically, given a batch of meshes it builds a
BVH tree for each one, which can then be used for distance quries.
## Installation
Before installing anything please make sure to set the environment variable
_$CUDA_SAMPLES_INC_ to the path that contains the header `helper_math.h` , which
can be found in the [CUDA Samples repository](https://github.com/NVIDIA/cuda-samples).
To install the module run the following commands:
**1. Install the dependencies**
```Shell
pip install -r requirements.txt
```
**2. Run the _setup.py_ script**
```Shell
python setup.py install
```
If you want to modify any part of the code then use the following command:
```Shell
python setup.py build develop
```
## Examples
- [Random points to surface](./examples/random_points_to_surface.py): Generate
random points and compute their distance to a mesh. Use:
```Shell
python examples/random_points_to_surface.py --mesh-fn MESH_FN --num-query-points 100000
```
- [Fit a cube to a cube](./examples/fit_cube_to_cube.py): Randomly translate
and rotate a cube then fit it to the original, without using the
correspondences by using the point to mesh distances.
```Shell
python examples/fit_cube_to_cube.py
```
- [Fit a cube to random points](./examples/fit_cube_to_random_points.py):
First generate a set of random points and compute their convex hull, which
gives us a dummy scan. We then try to rigidly align a cube to this scan using
the provided point-to-mesh residuals.
```Shell
python examples/fit_cube_to_random_points.py
```
## Dependencies
1. [PyTorch](https://pytorch.org)
## Example dependencies
1. [open3d](http://www.open3d.org/)
1. [mesh](https://github.com/MPI-IS/mesh)
## Running on Cluster
If you want to run this on the cluster you need to build it using the GPU availabe on the cluster. If you use the local build there might be GPU architecture compatibility issue and you can encounter following error message
```
RuntimeError: parallel_for failed: unrecognized error code: unrecognized error code
```
## Citation
If you find this code useful in your research please cite the relevant work(s) of the following list:
```
@inproceedings{Karras:2012:MPC:2383795.2383801,
author = {Karras, Tero},
title = {Maximizing Parallelism in the Construction of BVHs, Octrees, and K-d Trees},
booktitle = {Proceedings of the Fourth ACM SIGGRAPH / Eurographics Conference on High-Performance Graphics},
year = {2012},
pages = {33--37},
numpages = {5},
url = {https://doi.org/10.2312/EGGH/HPG12/033-037},
doi = {10.2312/EGGH/HPG12/033-037},
publisher = {Eurographics Association}
}
```
## Contact
The code of this repository was implemented by [Vassilis Choutas]([email protected]).
For commercial licensing, please contact [[email protected]]([email protected]).