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SORT3D: Spatial Object-centric Reasoning Toolbox for Zero-Shot 3D Grounding Using Large Language Models

Created by
  • Haebom

Author

Nader Zantout, Haochen Zhang, Pujith Kachana, Jinkai Qiu, Guofei Chen, Ji Zhang, Wenshan Wang

Outline

SORT3D proposes a method for interpreting object reference language and specifying objects using spatial relationships and properties in 3D environments for robots working with humans. Unlike existing methods that struggle with the complexity of diverse scenes, numerous fragmented objects, and free-form language references, SORT3D leverages the rich object properties of 2D data and combines a heuristic-based spatial inference toolbox with the sequential inference capabilities of large-scale language models (LLMs). It does not require training using text-to-3D data and can be applied zero-shot to new environments. In two benchmarks, we achieve state-of-the-art zero-shot performance on complex view-dependent grounding tasks, and by implementing a pipeline running in real-time on two autonomous vehicles, we demonstrate its applicability for object target exploration in previously unseen real-world environments. The source code is publicly available.

Takeaways, Limitations

Takeaways:
Improving zero-shot performance by combining rich object properties of 2D data with the sequential inference capabilities of LLM.
Solves the data shortage problem by eliminating the need for text-3D data learning.
Demonstrating the feasibility of object target exploration in real environments.
Achieving cutting-edge zero-shot performance.
Open source release for improved accessibility.
Limitations:
Additional validation of generalization performance in environments other than the presented benchmarks is needed.
Potential performance degradation due to limitations of heuristic-based spatial inference.
Possible vulnerability to complex language expressions.
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