Light-SQ

Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes

SIGGRAPH Asia 2025 TL;DR We present Light-SQ, a novel superquadric-based optimization framework that explicitly emphasizes structure-awareness.

Project Page

TODO

• Release V1 pytorch implementation with GPU acceleration, supporting with and without convex decomposition guidance.
• Provide disjoint region multi-process parallel acceleration.
• Multiscale fitting script.
• Structure-aware CoACD code release.
• Introduce support for latest convex decomposition methods, such as Learning-Convex-Decomposition.

Pull requests for the above features are very welcome!

Requirements

The installation process below has been tested on a Linux machine with NVIDIA T4 GPU from scratch.

1. Basic Installation, cuda-12.4 as an example

conda create -n lightsq python=3.11
conda activate lightsq
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt

2. CHECK your installed open3d version

If you are using open3d 0.18.0, you can continue with numpy-2, and make sure you install kaolin==0.18.0

pip install kaolin==0.18.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-{TORCH_VERSION}_cu{CUDA_VERSION}.html

# For the version stated above (cu124, torch-2.5.1)
pip install kaolin==0.18.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.5.1_cu124.html

3. Move to pytorch_minimize and install it.

NOTE: The implementation in this folder differs from the official pytorch minimize. It includes the TRF solver with bound constraints required by our fitting algorithm.

cd pytorch_minimize
pip install -e .

Execution

Run Light-SQ without convex decomposition guidance

1. SDF computation

• in_data_dir can be either a folder containing meshes or a mesh filepath.
• out_data_dir is the output root, for [name].glb, the sdf and visualizations will be stored to [out_data_dir]/[name]/sdf_extraction

python prepare_sdf_cuda.py -i [in_data_dir] -o [output_folder] --device cuda:0 --grid_resolution 100 [--save_viz]

# Example using data/test.glb
python prepare