ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

• URL: http://arxiv.org/abs/2402.17766v3
• Date: Fri, 12 Jul 2024 15:36:15 GMT
• Title: ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
• Authors: Zekun Qi, Runpei Dong, Shaochen Zhang, Haoran Geng, Chunrui Han, Zheng Ge, Li Yi, Kaisheng Ma,
• Abstract summary: This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction. ShapeLLM is built upon an improved 3D encoder by extending ReCon to ReCon++. ShapeLLM is trained on constructed instruction-following data and tested on our newly human-curated benchmark, 3D MM-Vet.
• Score: 37.0434133128805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. ShapeLLM is built upon an improved 3D encoder by extending ReCon to ReCon++ that benefits from multi-view image distillation for enhanced geometry understanding. By utilizing ReCon++ as the 3D point cloud input encoder for LLMs, ShapeLLM is trained on constructed instruction-following data and tested on our newly human-curated benchmark, 3D MM-Vet. ReCon++ and ShapeLLM achieve state-of-the-art performance in 3D geometry understanding and language-unified 3D interaction tasks, such as embodied visual grounding. Project page: https://qizekun.github.io/shapellm/

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