MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds

BingQuan Dai, Luo Li, Qihong Tang, Jie Wang, Xinyu Lian, Hao Xu, Minghan Qin, Xudong XU, Bo Dai, Haoqian Wang, Zhaoyang Lyu, Jiangmiao Pang

Keywords: 3D reconstruction, point cloud, LLM

TL;DR: We trained MeshLLM, an Object-to-Code inference framework that generates Blender Python scripts to reconstruct 3D meshes from point clouds in a structured and editable manner.

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 MeshLLM, 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 performance in shape-to-code reconstruction tasks but also facilitates intuitive geometric and topological editing through convenient code modifications. Furthermore, our code-based representation enhances the reasoning capabilities of LLMs in 3D shape understanding tasks. Together, these contributions establish MeshLLM as a powerful and flexible solution for programmatic 3D shape reconstruction and understanding.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 3624