Conquer3D

Installation

1. Install from PyPI

pip install -U conquer3d

2. Install via Docker (Recommended for complete 3D & CUDA setup)

You can directly pull and run the pre-built Docker image with full GPU and CUDA toolchain support:

docker pull kohido/conquer3d:latest
docker run --rm --gpus all -it kohido/conquer3d:latest bash

Or build the image locally from source:

docker build -t conquer3d:latest .
docker run --rm --gpus all -it conquer3d:latest bash

3. Build from Source

To build from source, ensure you have a compatible CUDA toolchain (e.g., CUDA Toolkit 12.8) and PyTorch installed:

# Optional: Create a dedicated Conda environment with modern C++ compilers and CUDA
conda create -c conda-forge -n geocutool python=3.10 gxx_linux-64=13 gcc_linux-64=13 -y
conda activate geocutool
conda install -c conda-forge sparsehash -y
conda install nvidia::cuda-toolkit==12.8.2 -y

# Install PyTorch and binding generators
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install pybind11-stubgen

Install directly from GitHub without build isolation:

pip install git+https://github.com/KhoiDOO/geocutool.git --no-build-isolation

git clone https://github.com/KhoiDOO/geocutool.git
cd geocutool
pip install -e . --no-build-isolation

Features

Isosurface Extraction: Differentiable Marching Cubes, Marching Cubes Asymptotic (MCA), Marching Tetrahedra (MT), Dual Contouring (DC) with GPU QEF solver, and Dual Marching Cubes (DMC) for multi-sheet manifold topological resolution with pure quad / Delaunay triangle extraction.
Spatial Acceleration Structures: GPU Bounding Volume Hierarchies (Mesh BVH, GS BVH, PGS BVH), KD-Trees, Morton Z-Curve sorting, and 3D Sparse Voxel Grids.
Differentiable Ray Tracing & Radiance Fields: Fast 3D Gaussian Splatting (3DGS) and Periodic Gaussian Splatting (PGS) query kernels.
Geometric Metrics & Integrals: Exact one-sided & symmetric Chamfer / Hausdorff distances and single-view volume integrals.

Acknowledgements & References

For further theoretical background, GPU collision detection guides, and related open-source projects, please refer to:

Research Papers: Key computational geometry, differential topology, and acceleration structure literature.
Blog Posts: Articles and guides on NVIDIA GPU spatial traversal and parallel construction.
Related Repositories: Open-source libraries and frameworks supporting geometric deep learning and processing.
Books: Core theoretical books.

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