Full Code of carlinds/splatad for AI

Repository: carlinds/splatad
Branch: main
Commit: 6e31ad766d39
Files: 52
Total size: 2.4 MB

Directory structure:
gitextract_en0yens4/

├── .clang-format
├── .clangd_template
├── .github/
│   └── workflows/
│       ├── building.yml
│       ├── core_tests.yml
│       └── cuda/
│           ├── Linux-env.sh
│           ├── Linux.sh
│           ├── Windows-env.sh
│           └── Windows.sh
├── .gitignore
├── .gitmodules
├── LICENSE
├── MANIFEST.in
├── README.md
├── assets/
│   └── test_garden.npz
├── build_extensions.py
├── examples/
│   ├── lidar_rendering.ipynb
│   └── rolling_shutter.ipynb
├── gsplat/
│   ├── __init__.py
│   ├── _helper.py
│   ├── compression/
│   │   ├── __init__.py
│   │   ├── png_compression.py
│   │   └── sort.py
│   ├── cuda/
│   │   ├── __init__.py
│   │   ├── _backend.py
│   │   ├── _torch_impl.py
│   │   ├── _wrapper.py
│   │   └── csrc/
│   │       ├── bindings.h
│   │       ├── ext.cpp
│   │       ├── helpers.cuh
│   │       ├── projection.cu
│   │       ├── rasterization.cu
│   │       ├── relocation.cu
│   │       ├── sh.cu
│   │       └── utils.cuh
│   ├── distributed.py
│   ├── profile.py
│   ├── relocation.py
│   ├── rendering.py
│   ├── strategy/
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── default.py
│   │   ├── mcmc.py
│   │   └── ops.py
│   ├── utils.py
│   └── version.py
├── pyproject.toml
├── setup.py
└── tests/
    ├── _test_distributed.py
    ├── test_basic.py
    ├── test_compression.py
    ├── test_rasterization.py
    └── test_strategy.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .clang-format
================================================
BasedOnStyle: LLVM
AlignAfterOpenBracket: BlockIndent
BinPackArguments: false
BinPackParameters: false
IndentWidth: 4


================================================
FILE: .clangd_template
================================================
# To set up the clangd config.
# 1. Activate an environment with cuda installed. (probably via conda, skip if using system CUDA)
# 2. Run:
#       echo "# Autogenerated, see .clangd_template\!" > .clangd && sed -e "/^#/d" -e "s|YOUR_CUDA_PATH|$(dirname $(dirname $(which nvcc)))|" .clangd_template >> .clangd
CompileFlags:
  Add:
    - -Xclang
    - -fcuda-allow-variadic-functions 
    - --cuda-path=YOUR_CUDA_PATH
  Remove: 
    - --diag_suppress=*
    - --generate-code=*
    - -gencode=*
    - -forward-unknown-to-host-compiler
    - -Xcompiler
    - -Xcudafe
    - --use_fast_math
    - --options-file
    - --compiler-options
    - --expt-relaxed-constexpr


================================================
FILE: .github/workflows/building.yml
================================================
name: Building Wheels

on: [workflow_dispatch]

jobs:

  wheel:
    runs-on: ${{ matrix.os }}
    environment: production

    strategy:
      fail-fast: false
      matrix:
        # os: [windows-2019]
        # python-version: ['3.7']
        # torch-version: [1.11.0]
        # cuda-version: ['cu113']
        os: [ubuntu-20.04, windows-2019]
        # support version based on: https://download.pytorch.org/whl/torch/
        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
        torch-version: [1.11.0, 1.12.0, 1.13.0, 2.0.0]
        cuda-version: ['cu113', 'cu115', 'cu116', 'cu117', 'cu118']
        exclude:
          - torch-version: 1.11.0
            python-version: '3.11'
          - torch-version: 1.11.0
            cuda-version: 'cu116'
          - torch-version: 1.11.0
            cuda-version: 'cu117'
          - torch-version: 1.11.0
            cuda-version: 'cu118'
          
          - torch-version: 1.12.0
            python-version: '3.11'
          - torch-version: 1.12.0
            cuda-version: 'cu115'
          - torch-version: 1.12.0
            cuda-version: 'cu117'
          - torch-version: 1.12.0
            cuda-version: 'cu118'
          
          - torch-version: 1.13.0
            cuda-version: 'cu102'
          - torch-version: 1.13.0
            cuda-version: 'cu113'
          - torch-version: 1.13.0
            cuda-version: 'cu115'
          - torch-version: 1.13.0
            cuda-version: 'cu118'
            
          - torch-version: 2.0.0
            python-version: '3.7'
          - torch-version: 2.0.0
            cuda-version: 'cu102'
          - torch-version: 2.0.0
            cuda-version: 'cu113'
          - torch-version: 2.0.0
            cuda-version: 'cu115'
          - torch-version: 2.0.0
            cuda-version: 'cu116'
          
          - os: windows-2019
            cuda-version: 'cu102'
          - os: windows-2019
            torch-version: 1.13.0
            python-version: '3.11'
          
          # - os: windows-2019
          #   torch-version: 1.13.0
          #   cuda-version: 'cu117'
          #   python-version: '3.9'



    steps:
      - uses: actions/checkout@v3
        with:
          submodules: 'recursive'

      - name: Set up Python ${{ matrix.python-version }}
        uses: actions/setup-python@v4
        with:
          python-version: ${{ matrix.python-version }}

      - name: Upgrade pip
        run: |
          pip install --upgrade setuptools
          pip install ninja

      - name: Free up disk space
        if: ${{ runner.os == 'Linux' }}
        run: |
          sudo rm -rf /usr/share/dotnet

      - name: Install CUDA ${{ matrix.cuda-version }}
        if: ${{ matrix.cuda-version != 'cpu' }}
        run: |
          bash .github/workflows/cuda/${{ runner.os }}.sh ${{ matrix.cuda-version }}

      - name: Install PyTorch ${{ matrix.torch-version }}+${{ matrix.cuda-version }}
        run: |
          pip install torch==${{ matrix.torch-version }} --extra-index-url https://download.pytorch.org/whl/${{ matrix.cuda-version }}
          python -c "import torch; print('PyTorch:', torch.__version__)"
          python -c "import torch; print('CUDA:', torch.version.cuda)"
          python -c "import torch; print('CUDA Available:', torch.cuda.is_available())"

      - name: Patch PyTorch static constexpr on Windows
        if: ${{ runner.os == 'Windows' }}
        run: |
          Torch_DIR=`python -c 'import os; import torch; print(os.path.dirname(torch.__file__))'`
          sed -i '31,38c\
          TORCH_API void lazy_init_num_threads();' ${Torch_DIR}/include/ATen/Parallel.h
        shell: bash

      - name: Set version
        if: ${{ runner.os != 'macOS' }}
        run: |
          VERSION=`sed -n 's/^__version__ = "\(.*\)"/\1/p' gsplat/version.py`
          TORCH_VERSION=`echo "pt${{ matrix.torch-version }}" | sed "s/..$//" | sed "s/\.//g"`
          CUDA_VERSION=`echo ${{ matrix.cuda-version }}`
          echo "New version name: $VERSION+$TORCH_VERSION$CUDA_VERSION"
          sed -i "s/$VERSION/$VERSION+$TORCH_VERSION$CUDA_VERSION/" gsplat/version.py
        shell:
          bash

      - name: Install main package for CPU
        if: ${{ matrix.cuda-version == 'cpu' }}
        run: |
          BUILD_NO_CUDA=1 pip install .

      - name: Build wheel
        run: |
          pip install wheel
          source .github/workflows/cuda/${{ runner.os }}-env.sh ${{ matrix.cuda-version }}
          python setup.py bdist_wheel --dist-dir=dist
        shell: bash  # `source` does not exist in windows powershell

      - name: Test wheel
        run: |
          cd dist
          ls -lah
          pip install *.whl
          python -c "import gsplat; print('gsplat:', gsplat.__version__)"
          cd ..
        shell: bash  # `ls -lah` does not exist in windows powershell


================================================
FILE: .github/workflows/core_tests.yml
================================================
name: Core Tests.

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

permissions:
  contents: read

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3
        with:
          submodules: 'recursive'
          
      - name: Set up Python 3.8.12
        uses: actions/setup-python@v4
        with:
          python-version: "3.8.12"
      - name: Install dependencies
        run: |
          pip install pytest
          pip install torch==2.0.0 --index-url https://download.pytorch.org/whl/cpu
          BUILD_NO_CUDA=1 pip install .
      - name: Run Tests.
        run: pytest tests/


================================================
FILE: .github/workflows/cuda/Linux-env.sh
================================================
#!/bin/bash

# Took from https://github.com/pyg-team/pyg-lib/

case ${1} in
  cu118)
    export CUDA_HOME=/usr/local/cuda-11.8
    export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
    export PATH=${CUDA_HOME}/bin:${PATH}
    export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
    ;;
  cu117)
    export CUDA_HOME=/usr/local/cuda-11.7
    export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
    export PATH=${CUDA_HOME}/bin:${PATH}
    export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
    ;;
  cu116)
    export CUDA_HOME=/usr/local/cuda-11.6
    export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
    export PATH=${CUDA_HOME}/bin:${PATH}
    export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
    ;;
  cu115)
    export CUDA_HOME=/usr/local/cuda-11.5
    export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
    export PATH=${CUDA_HOME}/bin:${PATH}
    export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
    ;;
  cu113)
    export CUDA_HOME=/usr/local/cuda-11.3
    export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
    export PATH=${CUDA_HOME}/bin:${PATH}
    export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6"
    ;;
  cu102)
    export CUDA_HOME=/usr/local/cuda-10.2
    export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
    export PATH=${CUDA_HOME}/bin:${PATH}
    export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5"
    ;;
  *)
    ;;
esac

================================================
FILE: .github/workflows/cuda/Linux.sh
================================================
#!/bin/bash

# Took from https://github.com/pyg-team/pyg-lib/

OS=ubuntu2004

case ${1} in
  cu118)
    CUDA=11.8
    APT_KEY=${OS}-${CUDA/./-}-local
    FILENAME=cuda-repo-${APT_KEY}_${CUDA}.0-520.61.05-1_amd64.deb
    URL=https://developer.download.nvidia.com/compute/cuda/${CUDA}.0/local_installers
    ;;
  cu117)
    CUDA=11.7
    APT_KEY=${OS}-${CUDA/./-}-local
    FILENAME=cuda-repo-${APT_KEY}_${CUDA}.1-515.65.01-1_amd64.deb
    URL=https://developer.download.nvidia.com/compute/cuda/${CUDA}.1/local_installers
    ;;
  cu116)
    CUDA=11.6
    APT_KEY=${OS}-${CUDA/./-}-local
    FILENAME=cuda-repo-${APT_KEY}_${CUDA}.2-510.47.03-1_amd64.deb
    URL=https://developer.download.nvidia.com/compute/cuda/${CUDA}.2/local_installers
    ;;
  cu115)
    CUDA=11.5
    APT_KEY=${OS}-${CUDA/./-}-local
    FILENAME=cuda-repo-${APT_KEY}_${CUDA}.2-495.29.05-1_amd64.deb
    URL=https://developer.download.nvidia.com/compute/cuda/${CUDA}.2/local_installers
    ;;
  cu113)
    CUDA=11.3
    APT_KEY=${OS}-${CUDA/./-}-local
    FILENAME=cuda-repo-${APT_KEY}_${CUDA}.0-465.19.01-1_amd64.deb
    URL=https://developer.download.nvidia.com/compute/cuda/${CUDA}.0/local_installers
    ;;
  cu102)
    CUDA=10.2
    APT_KEY=${CUDA/./-}-local-${CUDA}.89-440.33.01
    FILENAME=cuda-repo-${OS}-${APT_KEY}_1.0-1_amd64.deb
    URL=https://developer.download.nvidia.com/compute/cuda/${CUDA}/Prod/local_installers
    ;;
  *)
    echo "Unrecognized CUDA_VERSION=${1}"
    exit 1
    ;;
esac

wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-${OS}.pin
sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget -nv ${URL}/${FILENAME}
sudo dpkg -i ${FILENAME}

if [ "${1}" = "cu117" ] || [ "${1}" = "cu118" ]; then
  sudo cp /var/cuda-repo-${APT_KEY}/cuda-*-keyring.gpg /usr/share/keyrings/
else
  sudo apt-key add /var/cuda-repo-${APT_KEY}/7fa2af80.pub
fi

sudo apt-get update
sudo apt-get -y install cuda

rm -f ${FILENAME}

================================================
FILE: .github/workflows/cuda/Windows-env.sh
================================================
#!/bin/bash

# Took from https://github.com/pyg-team/pyg-lib/

case ${1} in
  cu118)
    CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.8
    PATH=${CUDA_HOME}/bin:$PATH
    PATH=/c/Program\ Files\ \(x86\)/Microsoft\ Visual\ Studio/2017/BuildTools/MSBuild/15.0/Bin:$PATH
    export TORCH_CUDA_ARCH_LIST="6.0+PTX"
    ;;
  cu117)
    CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.7
    PATH=${CUDA_HOME}/bin:$PATH
    PATH=/c/Program\ Files\ \(x86\)/Microsoft\ Visual\ Studio/2017/BuildTools/MSBuild/15.0/Bin:$PATH
    export TORCH_CUDA_ARCH_LIST="6.0+PTX"
    ;;
  cu116)
    CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.6
    PATH=${CUDA_HOME}/bin:$PATH
    PATH=/c/Program\ Files\ \(x86\)/Microsoft\ Visual\ Studio/2017/BuildTools/MSBuild/15.0/Bin:$PATH
    export TORCH_CUDA_ARCH_LIST="6.0+PTX"
    ;;
  cu115)
    CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.5
    PATH=${CUDA_HOME}/bin:$PATH
    PATH=/c/Program\ Files\ \(x86\)/Microsoft\ Visual\ Studio/2017/BuildTools/MSBuild/15.0/Bin:$PATH
    export TORCH_CUDA_ARCH_LIST="6.0+PTX"
    ;;
  cu113)
    CUDA_HOME=/c/Program\ Files/NVIDIA\ GPU\ Computing\ Toolkit/CUDA/v11.3
    PATH=${CUDA_HOME}/bin:$PATH
    PATH=/c/Program\ Files\ \(x86\)/Microsoft\ Visual\ Studio/2017/BuildTools/MSBuild/15.0/Bin:$PATH
    export TORCH_CUDA_ARCH_LIST="6.0+PTX"
    ;;
  *)
    ;;
esac

================================================
FILE: .github/workflows/cuda/Windows.sh
================================================
#!/bin/bash

# Took from https://github.com/pyg-team/pyg-lib/

# Install NVIDIA drivers, see:
# https://github.com/pytorch/vision/blob/master/packaging/windows/internal/cuda_install.bat#L99-L102
curl -k -L "https://drive.google.com/u/0/uc?id=1injUyo3lnarMgWyRcXqKg4UGnN0ysmuq&export=download" --output "/tmp/gpu_driver_dlls.zip"
7z x "/tmp/gpu_driver_dlls.zip" -o"/c/Windows/System32"

case ${1} in
  cu118)
    CUDA_SHORT=11.8
    CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.0/local_installers
    CUDA_FILE=cuda_${CUDA_SHORT}.0_522.06_windows.exe
    ;;
  cu117)
    CUDA_SHORT=11.7
    CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.1/local_installers
    CUDA_FILE=cuda_${CUDA_SHORT}.1_516.94_windows.exe
    ;;
  cu116)
    CUDA_SHORT=11.3
    CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.0/local_installers
    CUDA_FILE=cuda_${CUDA_SHORT}.0_465.89_win10.exe
    ;;
  cu115)
    CUDA_SHORT=11.3
    CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.0/local_installers
    CUDA_FILE=cuda_${CUDA_SHORT}.0_465.89_win10.exe
    ;;
  cu113)
    CUDA_SHORT=11.3
    CUDA_URL=https://developer.download.nvidia.com/compute/cuda/${CUDA_SHORT}.0/local_installers
    CUDA_FILE=cuda_${CUDA_SHORT}.0_465.89_win10.exe
    ;;
  *)
    echo "Unrecognized CUDA_VERSION=${1}"
    exit 1
    ;;
esac

curl -k -L "${CUDA_URL}/${CUDA_FILE}" --output "${CUDA_FILE}"
echo ""
echo "Installing from ${CUDA_FILE}..."
PowerShell -Command "Start-Process -FilePath \"${CUDA_FILE}\" -ArgumentList \"-s nvcc_${CUDA_SHORT} cuobjdump_${CUDA_SHORT} nvprune_${CUDA_SHORT} cupti_${CUDA_SHORT} cublas_dev_${CUDA_SHORT} cudart_${CUDA_SHORT} cufft_dev_${CUDA_SHORT} curand_dev_${CUDA_SHORT} cusolver_dev_${CUDA_SHORT} cusparse_dev_${CUDA_SHORT} thrust_${CUDA_SHORT} npp_dev_${CUDA_SHORT} nvrtc_dev_${CUDA_SHORT} nvml_dev_${CUDA_SHORT}\" -Wait -NoNewWindow"
echo "Done!"
rm -f "${CUDA_FILE}"

================================================
FILE: .gitignore
================================================
.clangd
compile_commands.json

# Visual Studio Code configs.
.vscode/

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
# lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/

.DS_Store

# Direnv config.
.envrc

# line_profiler
*.lprof

*build
compile_commands.json
*.dump

data
results

================================================
FILE: .gitmodules
================================================
[submodule "gsplat/cuda/csrc/third_party/glm"]
	path = gsplat/cuda/csrc/third_party/glm
	url = https://github.com/g-truc/glm.git


================================================
FILE: LICENSE
================================================
                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

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      the copyright owner that is granting the License.

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      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
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      "You" (or "Your") shall mean an individual or Legal Entity
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================================================
FILE: MANIFEST.in
================================================
recursive-include gsplat/cuda/csrc *


================================================
FILE: README.md
================================================
<p align="center">
    <!-- project badges -->
    <a href="https://research.zenseact.com/publications/splatad/"><img src="https://img.shields.io/badge/Project-Page-ffa"/></a>
    <!-- paper badges -->
    <a href="https://arxiv.org/abs/2411.16816">
        <img src='https://img.shields.io/badge/arXiv-Page-aff'>
    </a>
</p>

<div align="center">
<h3 style="font-size:2.0em;">SplatAD</h3>
<h4 style="font-size:1.5em;">
Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
</h4>
</div>
<div align="center">

<div align="center">
<picture>
    <source media="(prefers-color-scheme: dark)" srcset="docs/_static/imgs/front-fig-stacked.jpg" />
    <img alt="tyro logo" src="docs/_static/imgs/front-fig-stacked.jpg" width="80%"/>
</picture>
</div>

[Project page](https://research.zenseact.com/publications/splatad/)

</div>


# About
This is the official repository for the CVPR 2025 paper [_SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving_](https://arxiv.org/abs/2411.16816). The code in this repository builds upon the open-source library [gsplat](https://github.com/nerfstudio-project/gsplat), with modifications and extensions designed for autonomous driving data. 

While the code contians all components needed to efficiently render camera and lidar data, the SplatAD-model itself, including dataloading, decoders, etc., will be released through [neurad-studio](https://github.com/georghess/neurad-studio).

**We welcome all contributions!**

# Key Features
- Efficient lidar rendering
    - Projection to spherical coordinates
    - Depth and feature rasterization for a non-linear grid of points
- Rolling shutter compensation for camera and lidar


# Installation
Our code introduce no additional dependencies. We thus refer to the original documentation from gsplat for both [installation](https://github.com/nerfstudio-project/gsplat#installation) and [development setup](https://github.com/nerfstudio-project/gsplat/blob/main/docs/DEV.md).

# Usage
See [`rasterization`](gsplat/rendering.py#L22) and [`lidar_rasterization`]((gsplat/rendering.py#L443)) for entry points to camera and lidar rasterization.
Additionally, we provide example notebooks under [examples](examples) that demonstrate lidar rendering and rolling shutter compensation.
For further examples, check out the [test files](tests).


# Built On
- [gsplat](https://github.com/nerfstudio-project/gsplat) - Collaboration friendly library for CUDA accelerated rasterization of Gaussians with python bindings
- [3dgs-deblur](https://github.com/SpectacularAI/3dgs-deblur) - Inspiration for the rolling shutter compensation

# Citation

You can find our paper on [arXiv](https://arxiv.org/abs/2411.16816).

If you use this code or find our paper useful, please consider citing:

```bibtex
@article{hess2024splatad,
  title={SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving},
  author={Hess, Georg and Lindstr{\"o}m, Carl and Fatemi, Maryam and Petersson, Christoffer and Svensson, Lennart},
  journal={arXiv preprint arXiv:2411.16816},
  year={2024}
}
```

# Contributors

<a href="https://github.com/georghess">
    <img src="https://github.com/georghess.png" width="60px;" style="border-radius: 50%;"/>
</a>
<a href="https://github.com/carlinds">
    <img src="https://github.com/carlinds.png" width="60px;" style="border-radius: 50%;"/>
</a>

\+ [gsplat contributors](https://github.com/nerfstudio-project/gsplat/graphs/contributors)

================================================
FILE: build_extensions.py
================================================
import os
import subprocess
import shutil
from pathlib import Path
import argparse

parser = argparse.ArgumentParser(description="Build and cache gsplat CUDA extensions.")
parser.add_argument(
    "--build-root",
    type=Path,
    required=True,
    help="Path to the central build cache directory (e.g. /shared/gsplat_builds)",
)

def get_commit_hash():
    return subprocess.check_output(["git", "rev-parse", "--short", "HEAD"], text=True).strip()

def main():
    args = parser.parse_args()
    repo_root = Path(__file__).resolve().parent
    build_root: Path = args.build_root
    commit = get_commit_hash()
    build_dir = build_root / commit

    if build_dir.exists():
        print(f"✅ Build for {commit} already exists at {build_dir}")
        return

    print(f"🔨 Building gsplat extensions for commit {commit} ...")
    build_tmp = repo_root / "build_tmp"
    if build_tmp.exists():
        shutil.rmtree(build_tmp)

    os.environ["BUILD_CUDA"] = "1"
    subprocess.check_call(
        ["python", "setup.py", "build_ext", f"--build-lib={build_tmp}"],
        cwd=repo_root
    )

    build_dir.mkdir(parents=True, exist_ok=True)
    for so_file in build_tmp.rglob("*.so"):
        rel = so_file.relative_to(build_tmp)
        dest = build_dir / rel
        dest.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy2(so_file, dest)
        print(f"📦 Copied {rel}")

    shutil.rmtree(build_tmp)
    print(f"✅ Done. Stored build at: {build_dir}")

if __name__ == "__main__":
    main()


================================================
FILE: examples/lidar_rendering.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demo of lidar_rasterization usage\n",
    "\n",
    "This notebook shows a minimal example of how to render a lidar point cloud. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from gsplat.rendering import lidar_rasterization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "assert torch.cuda.is_available(), \"These examples require a GPU.\"\n",
    "DEVICE = torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up the Gaussians\n",
    "Let's define some random Gaussians to create a scene to render. We'll place the Gaussians in random directions, at a distance between 20 and 120 meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Num lidars, num Gaussians\n",
    "C, N = 1, 1_000_000\n",
    "\n",
    "# Define the Gaussians\n",
    "means = (torch.rand(N, 1, device=DEVICE)*100 + 20) * F.normalize(torch.randn(N, 3, device=DEVICE), dim=-1) # random range * random direction\n",
    "quats = F.normalize(torch.randn(N, 4, device=DEVICE))\n",
    "scales = torch.rand(N, 3, device=DEVICE) + 0.1\n",
    "opacities = torch.rand(N, device=DEVICE) \n",
    "features = torch.randn(N, 16, device=DEVICE)\n",
    "velocities = torch.randn(N, 3, device=DEVICE) * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up the lidar\n",
    "We define a lidar with 0.2 degrees between beams in the azimuth direction and with 32 elevation channels between -30 and +30 degrees in elevation. **Note**: We define elevation as the angle against the horizontal xy-plane, which is different from other definitions of spherical coordinates.\n",
    "\n",
    "For inference, we generate points according to the lidar specs, whereas during training we use actual points. To simulate real data, here we add some noise to the observed azimuth and elevation, and drop points beyond a certain range.\n",
    "\n",
    "Note that the points should be defined in the lidar coordinate system _when they were collected_. In other words, ego-motion compensation should be removed prior to running the rasterization if using real data. The movement of the lidar during capture should be provided as input to the rasterization function call. This way, we render the distance between the sensor and surfaces at the time the ray was shot, and can easily simulate different collection rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the lidar parameters\n",
    "azimuth_resolution = 0.2 # degrees\n",
    "min_azimuth = -180 # degrees \n",
    "max_azimuth = 180 # degrees\n",
    "n_elevation_channels = 32\n",
    "min_elevation = -30 # degrees\n",
    "max_elevation = 30 # degrees\n",
    "\n",
    "# Generate the lidar points to be rasterized\n",
    "azimuths = torch.linspace(\n",
    "    min_azimuth,\n",
    "    max_azimuth - azimuth_resolution,\n",
    "    int((max_azimuth - azimuth_resolution - min_azimuth) / azimuth_resolution) + 1,\n",
    "    device=DEVICE\n",
    ")\n",
    "elevations = torch.linspace(\n",
    "    min_elevation,\n",
    "    max_elevation,\n",
    "    n_elevation_channels,\n",
    "    device=DEVICE\n",
    ")\n",
    "azim_elev = torch.meshgrid(\n",
    "    azimuths,\n",
    "    elevations,\n",
    "    indexing=\"ij\"\n",
    ")\n",
    "azim_elev = torch.stack(azim_elev, dim=-1) # (n_azimuth_channels, n_elevation_channels, 2)\n",
    "azim_elev += torch.randn_like(azim_elev) * 0.001 # add some noise to the points\n",
    "pc_range = torch.randn_like(azim_elev[...,0]).abs() * 50 + 2 # random range for the points\n",
    "pc_azim_elev_range = torch.cat([\n",
    "    azim_elev,\n",
    "    pc_range.unsqueeze(-1)\n",
    "], dim=-1) # (n_azimuth_channels, n_elevation_channels, 3)\n",
    "\n",
    "######## remember to convert to radians for going to xyz ########\n",
    "pc_xyz = torch.stack([\n",
    "    torch.cos(azim_elev[...,1].deg2rad()) * torch.cos(azim_elev[...,0].deg2rad()) * pc_range,\n",
    "    torch.cos(azim_elev[...,1].deg2rad()) * torch.sin(azim_elev[...,0].deg2rad()) * pc_range,\n",
    "    torch.sin(azim_elev[...,1].deg2rad()) * pc_range\n",
    "], dim=-1) # (n_azimuth_channels, n_elevation_channels, 3)\n",
    "\n",
    "range_filter = pc_range <= pc_range.quantile(0.7) # pretend points beyond some range are not visible\n",
    "pc_xyz = pc_xyz[range_filter] # (n_points, 3)\n",
    "pc_azim_elev_range = pc_azim_elev_range[range_filter] # (n_points, 3)\n",
    "\n",
    "pc_timeoffset = (torch.rand_like(pc_azim_elev_range[...,0:1]) - 0.5) * 0.1 # offset per point from timestamp of the scan\n",
    "pc_intensity = torch.rand_like(pc_azim_elev_range[...,0:1]) # random intensity for the points\n",
    "point_cloud = torch.cat([\n",
    "    pc_azim_elev_range,\n",
    "    pc_timeoffset,\n",
    "    pc_intensity,\n",
    "], dim=-1) # (n_points, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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