MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
Bingquan Dai, Li Ray Luo, Qihong Tang, Jie Wang, Xinyu Lian, Hao Xu, Minghan Qin, Xudong Xu, Bo Dai, Haoqian Wang, Zhaoyang Lyu, Jiangmiao Pang

TL;DR
MeshCoder is a novel framework that uses large language models and expressive Blender Python APIs to reconstruct complex 3D objects from point clouds into editable scripts, enabling advanced shape editing and understanding.
Contribution
It introduces a comprehensive API set, constructs a large-scale dataset, and trains an LLM for accurate 3D shape-to-code reconstruction, surpassing existing methods.
Findings
Achieves superior shape-to-code reconstruction performance.
Enables intuitive geometric and topological editing.
Enhances 3D shape understanding through code-based reasoning.
Abstract
Reconstructing 3D objects into editable programs is pivotal for applications like reverse engineering and shape editing. However, existing methods often rely on limited domain-specific languages (DSLs) and small-scale datasets, restricting their ability to model complex geometries and structures. To address these challenges, we introduce MeshCoder, a novel framework that reconstructs complex 3D objects from point clouds into editable Blender Python scripts. We develop a comprehensive set of expressive Blender Python APIs capable of synthesizing intricate geometries. Leveraging these APIs, we construct a large-scale paired object-code dataset, where the code for each object is decomposed into distinct semantic parts. Subsequently, we train a multimodal large language model (LLM) that translates 3D point cloud into executable Blender Python scripts. Our approach not only achieves superior…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
