{"ID":2880351,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14879","arxiv_id":"2508.14879","title":"MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds","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 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 MeshCoder as a powerful and flexible solution for programmatic 3D shape reconstruction and understanding. The project homepage is available at \\href{https://daibingquan.github.io/MeshCoder}{this link}.","short_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 chall...","url_abs":"https://arxiv.org/abs/2508.14879","url_pdf":"https://arxiv.org/pdf/2508.14879v2","authors":"[\"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\"]","published":"2025-08-20T17:50:15Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.CV\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}