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OMAR: Open-World Multi-Attribute Reasoning for Scene-Conditioned Shelf Organization

An arXiv paper proposes OMAR, an open-world multi-attribute reasoning pipeline for inferring a shelf's latent organization and ranking placement positions for new objects without user preference instructions. A vision-language model first proposes object labels and attributes such as content and packaging type, which are grounded through segmentation, depth, and multi-view association; an LLM then reasons over an instance-level 3D scene graph to produce scene-consistent placement rankings, followed by geometric and kinematic feasibility checks before execution.

Part of the event: OMAR: A Shelf-Organization Pipeline for Open-World Multi-Attribute Reasoning

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