MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
Tsinghua University · Shanghai Artificial Intelligence Laboratory · Harbin Institute of Technology, Shenzhen · Beijing Institute of Technology · Fudan University · The Hong Kong University of Science and Technology (Guangzhou) · Tsinghua University, Tsinghua University · Shanghai AI Laboratory · Shanghai AI Lab · Tsinghua Shenzhen International Graduate School · Shanghai AI Laboratory
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摘要
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.