https://github.com/bigmat18/cuda-mesh-voxelization

GPU-accelerated pipeline for robust 3D mesh Boolean operations (CSG) using voxelized Signed Distance Fields (SDFs).
https://github.com/bigmat18/cuda-mesh-voxelization

computer-graphics cuda hpc mesh-processing voxel

Last synced: 16 days ago
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GPU-accelerated pipeline for robust 3D mesh Boolean operations (CSG) using voxelized Signed Distance Fields (SDFs).

Host: GitHub
URL: https://github.com/bigmat18/cuda-mesh-voxelization
Owner: bigmat18
Created: 2025-06-06T15:55:59.000Z (12 months ago)
Default Branch: main
Last Pushed: 2026-03-29T01:35:10.000Z (2 months ago)
Last Synced: 2026-03-29T04:29:46.465Z (2 months ago)
Topics: computer-graphics, cuda, hpc, mesh-processing, voxel
Language: C++
Homepage: https://www.canva.com/design/DAGtctetbj0/ZMeEuIOAW2boftsXtSnLZQ/edit?utm_content=DAGtctetbj0&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton
Size: 41.4 MB
Stars: 25
Watchers: 0
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# CUDA Mesh Voxelization

## 1. Introduction

**CUDA Mesh Voxelization** is a C++/CUDA library and CLI for 3D mesh voxelization, CSG operations, and Signed Distance Field (SDF) computation on the GPU. The project is designed for benchmarking, visualization, and manipulation of volumetric 3D data, with a particular focus on performance and extensibility.

### Features

- **Mesh Voxelization:** Converts 3D triangle meshes into solid or surface voxel grids.
- **CSG Operations:** Supports union, intersection, and difference operations between voxelized meshes.
- **SDF Calculation:** Computes the signed distance field using the Jump Flooding Algorithm (JFA).
- **CLI Application:** Command-line interface for batch processing and benchmarking.
- **Benchmarking:** Comparative analysis between sequential, OpenMP, and CUDA implementations.
- **Visualization:** Exports voxel grids for analysis and plotting.

## 2. Papers, Libraries, and Requirements

### Reference Papers and Algorithms

- [Fast Parallel Surface and Solid Voxelization on GPUs by Michael Schwarz and Hans-Peter Seidel (2010)](https://michael-schwarz.com/research/publ/files/vox-siga10.pdf)
- [Out-of-Core Construction of Sparse Voxel Octrees](https://graphics.cs.kuleuven.be/publications/BLD14OCCSVO)
- [Jump Flooding in GPU with Applications to Voronoi Diagram and Distance Transform](https://www.comp.nus.edu.sg/~tants/jfa/i3d06.pdf)

### Libraries Used

- [cxxopts](https://github.com/jarro2783/cxxopts) for CLI parsing
- CMake as the build system

### Software Requirements

- **C++:** >= C++23
- **CUDA Toolkit:** >= 12.0
- **CMake:** >= 3.20
- **Compiler:** GCC/Clang/MSVC with C++23 support

---

## 3. Installation and Usage

### Installation

```bash
git clone https://github.com/bigmat18/cuda-mesh-voxelization.git
cd cuda-mesh-voxelization
git submodule update --init --recursive
cmake -S . -B build
cmake --build build --config Release
```

### Basic Usage

Voxelize a mesh and export the result:

```bash
./build/Release/apps/cli/cli assets/bunny.obj -n 128 -e -t 2 -p 1 -s
```

**Main options:**
- `-n` number of voxels per grid side
- `-e` enable export
- `-t` algorithm type (0 = sequential, 1 = naive, 2 = tiled, 3 = OpenMP)
- `-p` CSG operation type (1 = union, 2 = intersection, 3 = difference)
- `-s` compute SDF

For all available options:
```bash
./build/Release/apps/cli/cli -h
```

---

## 4. Results

Stanford Armadillo
64 voxels/side

Stanford Dragon
256 voxels/side

Stanford Bunny
128 voxels/side