WarpConvNet

Overview

WarpConvNet is a high-performance library for 3D deep learning, built on NVIDIA's CUDA framework. It provides efficient implementations of:

• Point cloud processing
• Sparse voxel convolutions
• Attention mechanisms for 3D data
• Geometric operations and transformations

Minimal example (ModelNet-style)

import torch.nn as nn
from jaxtyping import Float
from torch import Tensor

from warpconvnet.geometry.types.points import Points
from warpconvnet.geometry.types.voxels import Voxels
from warpconvnet.nn.modules.point_conv import PointConv
from warpconvnet.nn.modules.sparse_conv import SparseConv3d
from warpconvnet.nn.modules.sequential import Sequential
from warpconvnet.geometry.coords.search.search_configs import RealSearchConfig
from warpconvnet.ops.reductions import REDUCTIONS

point_conv = Sequential(
    PointConv(24, 64, neighbor_search_args=RealSearchConfig("knn", knn_k=16)),
    nn.LayerNorm(64),
    nn.ReLU(),
)
sparse_conv = Sequential(
    SparseConv3d(64, 128, kernel_size=3, stride=2),
    nn.ReLU(),
)

coords: Float[Tensor, "B N 3"]  # noqa: F821
pc: Points = Points.from_list_of_coordinates(coords, encoding_channels=8, encoding_range=1)
pc = point_conv(pc)
vox: Voxels = pc.to_voxels(reduction=REDUCTIONS.MEAN, voxel_size=0.05)
vox = sparse_conv(vox)
dense: Tensor = vox.to_dense(channel_dim=1, min_coords=(-5, -5, -5), max_coords=(2, 2, 2))
# feed `dense` to your 3D CNN head for classification

See examples/modelnet.py for a full training script.

Installation

Recommend using uv to install the dependencies. When using uv, prepend with uv pip install ....

# Install PyTorch first (specify your CUDA version)
export CUDA=cu128  # For CUDA 12.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/${CUDA}

# Install core dependencies
pip install build ninja
pip install cupy-cuda12x  # use cupy-cuda11x for CUDA 11.x
pip install git+https://github.com/rusty1s/pytorch_scatter.git
pip install flash-attn --no-build-isolation

# Install warpconvnet from source
git clone https://github.com/NVlabs/WarpConvNet.git
cd WarpConvNet
git submodule update --init 3rdparty/cutlass
pip install .

# If this fails, please create an issue on https://github.com/NVlabs/WarpConvNet/issues and try running the following commands:
cd WarpConvNet
# Option 1
python setup.py build_ext --inplace
# Option 2
pip install -e . --no-deps

Available optional dependency groups:

• warpconvnet[dev]: Development tools (pytest, coverage, pre-commit)
• warpconvnet[docs]: Documentation building tools
• warpconvnet[models]: Additional dependencies for model training (wandb, hydra, etc.)

Directory Structure

./
├── 3rdparty/            # Third-party dependencies
│   └── cutlass/         # CUDA kernels
├── docker/              # Docker build files
├── docs/                # Documentation sources
├── examples/            # Example applications
├── scripts/             # Development utilities
├── tests/               # Test suite
│   ├── base/            # Core functionality tests
│   ├── coords/          # Coordinate operation tests
│   ├── features/        # Feature processing tests
│   ├── nn/              # Neural network tests
│   ├── csrc/            # C++/CUDA test utilities
│   └── types/           # Geometry type tests
└── warpconvnet/         # Main package
    ├── csrc/            # C++/CUDA extensions
    ├── dataset/         # Dataset utilities
    ├── geometry/        # Geometric operations
    │   ├── base/        # Core definitions
    │   ├── coords/      # Coordinate operations
    │   ├── features/    # Feature operations
    │   └── types/       # Geometry types
    ├── nn/              # Neural networks
    │   ├── functional/  # Neural network functions
    │   └── modules/     # Neural network modules
    ├── ops/             # Basic operations
    └── utils/           # Utility functions

For complete directory structure, run bash scripts/dir_struct.sh.

Quick Start

ModelNet Classification

python examples/modelnet.py

ScanNet Semantic Segmentation

pip install warpconvnet[models]
cd warpconvnet/models
python examples/scannet.py train.batch_size=12 model=mink_unet

Docker Usage

Build and run with GPU support:

# Build container
cd docker
docker build -t warpconvnet .

# Run container
docker run --gpus all \
    --shm-size=32g \
    -it \
    -v "/home/${USER}:/root" \
    -v "$(pwd):/workspace" \
    warpconvnet:latest

Development

Running Tests

# Run all tests
pytest tests/

# Run specific test suite
pytest tests/nn/
pytest tests/coords/

# Run with benchmarks
pytest tests/ --benchmark-only

Building Documentation

# Install documentation dependencies
uv pip install -r docs/requirements.txt

# Build docs
mkdocs build -f mkdocs-readthedocs.yml

# Serve locally
mkdocs serve -f mkdocs-readthedocs.yml

📖 Documentation: https://nvlabs.github.io/WarpConvNet/

The documentation is automatically built and deployed to GitHub Pages on every push to the main branch.

License

Apache 2.0

Citation

If you use this code in your research, please cite:

@misc{warpconvnet2025,
  author = {Chris Choy and NVIDIA Research},
  title = {WarpConvNet: High-Performance 3D Deep Learning Library},
  year = {2025},
  publisher = {NVIDIA Corporation},
  howpublished = {\url{https://github.com/NVlabs/warpconvnet}}
}