Reproduce “LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning” on OpenResearch
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About this paper
Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan and 1 more
LL3DA is an LLM-driven assistant that achieves omni-3D understanding, reasoning, and planning by directly processing 3D point clouds and integrating visual interactive prompts (clicks, bounding boxes). The model consistently outperforms prior specialized 3D vision-language models on benchmarks like 3D Dense Captioning and 3D Question Answering, demonstrating its robust capabilities as a unified 3D generalist agent.
Key insights
- The Q-Former-inspired Multi-Modal Transformer design, allowing early fusion and interaction-aware feature extraction between visual prompts, textual instructions, and 3D scene embeddings, significantly improves performance.
- Instruction tuning with auxiliary tasks (e.g., "describe and localize," "answer and localize") and visual clicks enhances model robustness and generalist capabilities across different 3D tasks.
- Visual interactive prompts, such as clicks on related objects, demonstrably improve 3D Question Answering performance by effectively resolving ambiguities inherent in textual queries.
- Training LL3DA as a generalist agent allows it to effectively distinguish and perform across various 3D tasks, and these generalist weights serve as a strong initialization for further fine-tuning.
Summary by alphaXiv.
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