This library is a 3D extension of torchvision, providing datasets, tensor types, transforms, ops, metrics, and visualization utilities for 3D perception tasks.
Documentation is available at vision3d.dev.
[!WARNING]
vision3dis in active early development. The API may change without notice and documentation may be incomplete.
vision3d is published on PyPI as a pre-built wheel and sdist.
The wheel is built against the
LibTorch Stable ABI
and statically links the CUDA runtime, so one wheel works for any
Python 3.12+, torch 2.10+, and any
NVIDIA driver that supports CUDA 12.8 or newer
(Linux driver ≥ 570).
We recommend using uv as your package manager:
uv add vision3d
Or with pip:
pip install vision3d
Clone the repository and sync the environment:
git clone https://github.com/yeetypete/vision3d.git
cd vision3d
uv sync --all-extras
uv sync compiles the C++/CUDA extension as part of installing the project. On
machines where CUDA is installed but no GPU is visible (for example, inside
containers), force a CUDA build with:
FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST="12.0+PTX" uv sync --all-extras
[!NOTE]
TORCH_CUDA_ARCH_LISTselects which NVIDIA compute capabilities to compile CUDA kernels for (e.g.12.0for RTX 50-series). See the PyTorch docs for the full syntax.
To produce a wheel locally:
uv build
By default uv build resolves torch from PyPI, which currently ships the cu130
variant. If your local CUDA toolkit is a different major version, point uv at
the matching PyTorch wheel index instead:
uv build --index https://download.pytorch.org/whl/cu128
Replace cu128 with whatever CUDA major version your installed CUDA toolkit
ships, e.g. cu130, cu132.
viz: pulls in rerun-sdk for the visualization utilities in vision3d.viz.Request it at install time, for example: uv add 'vision3d[viz]'.
Contributions are welcome! See CONTRIBUTING.md for how to get started.
vision3d is released under the
BSD 3-Clause License.
Python
87.2%
Cuda
6.9%
C++
5.5%
This library is a 3D extension of torchvision, providing datasets, tensor types, transforms, ops, metrics, and visualization utilities for 3D perception tasks.
Documentation is available at vision3d.dev.
[!WARNING]
vision3dis in active early development. The API may change without notice and documentation may be incomplete.
vision3d is published on PyPI as a pre-built wheel and sdist.
The wheel is built against the
LibTorch Stable ABI
and statically links the CUDA runtime, so one wheel works for any
Python 3.12+, torch 2.10+, and any
NVIDIA driver that supports CUDA 12.8 or newer
(Linux driver ≥ 570).
We recommend using uv as your package manager:
uv add vision3d
Or with pip:
pip install vision3d
Clone the repository and sync the environment:
git clone https://github.com/yeetypete/vision3d.git
cd vision3d
uv sync --all-extras
uv sync compiles the C++/CUDA extension as part of installing the project. On
machines where CUDA is installed but no GPU is visible (for example, inside
containers), force a CUDA build with:
FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST="12.0+PTX" uv sync --all-extras
[!NOTE]
TORCH_CUDA_ARCH_LISTselects which NVIDIA compute capabilities to compile CUDA kernels for (e.g.12.0for RTX 50-series). See the PyTorch docs for the full syntax.
To produce a wheel locally:
uv build
By default uv build resolves torch from PyPI, which currently ships the cu130
variant. If your local CUDA toolkit is a different major version, point uv at
the matching PyTorch wheel index instead:
uv build --index https://download.pytorch.org/whl/cu128
Replace cu128 with whatever CUDA major version your installed CUDA toolkit
ships, e.g. cu130, cu132.
viz: pulls in rerun-sdk for the visualization utilities in vision3d.viz.Request it at install time, for example: uv add 'vision3d[viz]'.
Contributions are welcome! See CONTRIBUTING.md for how to get started.
vision3d is released under the
BSD 3-Clause License.
Python
87.2%
Cuda
6.9%
C++
5.5%