A Global Hypothesis Verification Framework for 3D Object Recognition in Clutter.
Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a principled framework to tackle the complexity of our visual world by leveraging on a plurality of recognition paradigms and cues. Accordingly, we present a 3D object recognition pipeline deploying both global and local 3D features as well as shape and color. Thereby, and facilitated by the robustness of the verification process, diverse object hypotheses can be gathered and weak hypotheses need not be suppressed too early to trade sensitivity for specificity. Experiments demonstrate the effectiveness of our proposal, which significantly improves over the state-of-art and attains ideal performance (no false negatives, no false positives) on three out of the six most relevant and challenging benchmark datasets.
Vision4Robotics group (ACIN-Technical University of Vienna), Wien, Austria.
Aitor
A
Aldoma
CVLAB Group (DISI-University of Bologna), Bologna, Italy.
Federico
F
Tombari
CVLAB Group (DISI-University of Bologna), Bologna, Italy.
Luigi Di
LD
Stefano
Vision4Robotics group (ACIN-Technical University of Vienna), Wien, Austria.
Markus
M
Vincze