This work introduces prior-free relative 6D pose estimation, which lifts the assumption of knowing which object is to be posed within the scene, and formulates a novel method (PROSE) that finds coarse correspondences between object instances using multimodal foundation features, thus requiring no training.
Behdad Khodabandehloo, Andrea Caraffa, Davide Boscaini et al.· 0 citations
This work presents FunFlow6D, a novel flow matching-based formulation that leverages features from geometric and appearance foundation models for pose estimation, eliminating the need for task-specific encoders supervised on object-scene overlap and reducing supervision requirements and memory overhead.
Amir Hamza, Davide Boscaini, Fabio Poiesi· 0 citations
The transition toward high-mix low-volume manufacturing demands flexibility in robotic manipulation. However, conventional vision systems remain a bottleneck, requiring extensive data collection and model retraining whenever a new object is introduced to the production line. To overcome this rigidity, we present xperce...
M. Bortolon, Andrea Caraffa, Alice Fasoli et al.· arXiv.org· 0 citations
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