CompassAD benchmark and CompassNet framework for intent-driven affordance prediction on the appropriate object within multi-object 3D point clouds conditioned on natural language intent.
Affordancesam: Segment anything once more in affordance grounding.arXiv preprint arXiv:2504.15650
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.
AffordVLA improves VLA models for robotic manipulation by implicitly injecting affordance perception through feature alignment with a zero-shot teacher, claiming SOTA results in simulation and real-world tests.
citing papers explorer
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CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects
CompassAD benchmark and CompassNet framework for intent-driven affordance prediction on the appropriate object within multi-object 3D point clouds conditioned on natural language intent.
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AFUN: Towards an Affordance Foundation Model for Functionality Understanding
AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.
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AffordVLA: Injecting Affordance Representations into Vision-Language-Action Models via Implicit Feature Alignment
AffordVLA improves VLA models for robotic manipulation by implicitly injecting affordance perception through feature alignment with a zero-shot teacher, claiming SOTA results in simulation and real-world tests.