ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

ShapeLLM is a pioneering 3D Multimodal Large Language Model (LLM) that is specifically crafted for embodied interactions. The research showcases how the model incorporates 3D point clouds with languages to formulate a universal understanding of 3D objects. An improved 3D encoder, ReCon++, has been extended from its predecessor, ReCon, to enhance geometry understanding through multi-view image distillation.

• Model: ShapeLLM, based on ReCon++ encoder.
• Data: Instruction-following data and 3D MM-Vet evaluation benchmark.
• Performance: SOTA in 3D geometry understanding and language-unified 3D interaction tasks.
• Applications: Embodied visual grounding and 3D point cloud processing.
• Advancements: Enhanced by multi-view image distillation technique.

ShapeLLM’s contribution to 3D understanding in AI is significant, as it bridges the gap between geometric data and linguistic elements, opening new avenues for research in embodied AI interaction and multimodal reasoning. Read more