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MeshCoder COMMUNITY LICENSE AGREEMENT MeshCoder Release Date: November 3, 2025 All the data and code within this repo are under CC-BY-NC-SA 4.0.
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MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
Bingquan Dai*, Li Luo*, Qihong Tang,
Jie Wang, Xinyu Lian, Hao Xu, Minghan Qin, Xudong Xu, Bo Dai, Haoqian Wang†, Zhaoyang Lyu† Jiangmiao Pang
* Equal contribution
† Corresponding author
Project lead: Zhaoyang Lyu
Overview
MeshCoder is a framework that converts 3D point clouds into editable Blender Python scripts, enabling programmatic reconstruction and editing of complex human-made objects. It overcomes prior limitations by developing expressive APIs for modeling intricate geometries, building a large-scale dataset of 1 million object-code pairs across 41 categories, and training a multimodal LLM to generate accurate, part-segmented code from point clouds. The approach outperforms existing methods in reconstruction quality, supports intuitive shape and topology editing via code modifications, and enhances 3D reasoning capabilities in LLMs.
Usage
See the Github repository: https://github.com/InternRobotics/MeshCoder regarding installation, training and inference instructions.
config.yaml and shape_tokenizer.pt are the configuration file and pretrained weights of the shape tokenizer.
adapter_config.json and adapter_model.safetensors are the configuration file and pretrained weights of the LoRA model.
The folder Llama3.2-1B contains the original weights of the base model Llama3.2-1B.
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Citation
@article{dai2025meshcoder,
title={Meshcoder: Llm-powered structured mesh code generation from point clouds},
author={Dai, Bingquan and Luo, Li Ray and Tang, Qihong and Wang, Jie and Lian, Xinyu and Xu, Hao and Qin, Minghan and Xu, Xudong and Dai, Bo and Wang, Haoqian and others},
journal={arXiv preprint arXiv:2508.14879},
year={2025}
}
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