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An end-to-end 2D-to-3D pipeline that consolidates 2D semantic features for 3D segmentation and delivers the strongest zero-shot transfer to SUN RGB-D and SceneNN is developed.
Experimental results show that, compared with single-skill and unconstrained baselines that ignore scene disturbance, MS-MEM achieves higher mapping accuracy while substantially reducing scene disturbance, highlighting the synergistic effects of active viewpoint selection, push, and grasp actions.
Semantic-Dense Placement Planning (SDPP) is proposed, an accessibility-preserving approach that ranks candidate poses using a semantic-density score combining inter-object semantic similarity with spatial proximity and significantly improves semantic placement quality over state-of-the-art baselines.
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