ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
Abstract (arXiv)
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/
Key facts. Cited by 133 papers in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 162 of its 208 references that are in the map. First posted on arXiv 27 Feb 2024.
Builds on
162 of 208 references.
Closest ancestors · 5
Same direction, newest first
- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities22 Jan
- 2023ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation24 Dec
- 2023EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought24 May
- 2023PaLM-E: An Embodied Multimodal Language Model6 Mar
- 2021PointCLIP: Point Cloud Understanding by CLIP4 Dec
Other references · 157
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- 2019How Can We Know What Language Models Know?cited by 953
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Citing papers per year: 2024: 25; 2025: 61; 2026: 47.
133 papers.
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