https://github.com/li-plus/tinypt
A tiny path tracer accelerated by OpenMP & CUDA.
https://github.com/li-plus/tinypt
computer-graphics path-tracing ray-tracing rendering
Last synced: 25 days ago
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A tiny path tracer accelerated by OpenMP & CUDA.
- Host: GitHub
- URL: https://github.com/li-plus/tinypt
- Owner: li-plus
- License: mit
- Created: 2021-04-27T14:29:12.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2023-01-21T03:27:14.000Z (over 2 years ago)
- Last Synced: 2025-03-21T02:36:10.759Z (about 1 month ago)
- Topics: computer-graphics, path-tracing, ray-tracing, rendering
- Language: C++
- Homepage:
- Size: 15.4 MB
- Stars: 7
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TinyPT
A tiny path tracer accelerated by OpenMP & CUDA.
## Features
* [x] Global illumination rendering with path tracing.
* [x] Parallel rendering with OpenMP (on CPU) and CUDA (on nvidia GPU).
* [x] Complex triangle mesh model from Wavefront OBJ with MTL texture, accelerated by BVH.
* [x] Lambertian, metal, dielectric, and glossy (Cook-Torrance GGX) BxDFs.
* [x] Alpha texture mapping, and bump texture with height map.
* [x] Area light, point light, distant light, and environmental lighting.
## Getting Started
**Basic Usage**
Firstly, clone the repo recursively to your local environment.
```sh
git clone --recursive https://github.com/li-plus/tinypt.git && cd tinypt
# or
git clone https://github.com/li-plus/tinypt.git && cd tinypt && git submodule update --init
```
Install compilation dependencies and download necessary scene resources.
```sh
sudo apt install gcc g++ cmake make libeigen3-dev libopencv-dev libgflags-dev libgoogle-glog-dev python3-dev python3-pip
bash resource/get_resource.sh
```
Build the project.
```sh
cmake -B build .
cmake --build build -j
```
Render an example scene with two spheres in a box, which is taken from [smallpt](https://www.kevinbeason.com/smallpt/).
```sh
./bin/pt -device cuda -num_samples 1000 -save_path scene.png -scene cornell_sphere
```
**Python API**
Python binding is also available for TinyPT. To build and install the Python package for development, run:
```sh
cd python/
python3 setup.py develop
```
Run a Python example.
```sh
python3 examples/main.py --device cuda --num-samples 1000 --save-path scene.png --scene cornell_sphere
```
To build the wheel for distribution, run:
```sh
python3 setup.py bdist_wheel
```
**Docker**
For those who do not have root privilege, we offer an alternative to build & run TinyPT within a docker container. For GPU support, [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) should be further installed.
```sh
cd docker
docker build . -t tinypt:latest --build-arg CUDA=1
bash run.sh tinypt:latest
```
## More Examples
Following scenes are credited to [smallpt](https://www.kevinbeason.com/smallpt/) and [McGuire Computer Graphics Archive](https://casual-effects.com/data/).
|  |  |
| ------------------------------------------------ | -------------------------------------------- |
|  |  |
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## References
+ smallpt [[code]](https://www.kevinbeason.com/smallpt/)
+ scratchapixel [[blog]](https://www.scratchapixel.com/index.php)
+ Ray Tracing in One Weekend [[book]](https://raytracing.github.io/) [[code]](https://github.com/RayTracing/raytracing.github.io)
+ Physically Based Rendering:From Theory To Implementation [[book]](https://www.pbr-book.org/) [[code]](https://github.com/mmp/pbrt-v3)
+ minpt [[code]](https://github.com/hi2p-perim/minpt)