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A Use-Case Study on Multi-view Hypothesis Fusion for 3D Object Classification

Panagiotis Papadakis 1, 2
1 Lab-STICC_IMTA_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Object classification is a core element of various robot services ranging from environment mapping and object manipulation to human activity understanding. Due to limits in the robot configuration space or occlusions, a deeper understanding is needed on the potential of partial, multi-view based recognition. Towards this goal, we benchmark a number of schemes for hypothesis fusion under different environment assumptions and observation capacities, using a large-scale ground truth dataset and a baseline view-based recognition methodology. The obtained results highlight important aspects that should be taken into account when designing multi-view based recognition pipelines and converge to a hybrid scheme of enhanced performance as well as utility.
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https://hal.inria.fr/hal-01699827
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Submitted on : Friday, February 2, 2018 - 5:14:54 PM
Last modification on : Tuesday, April 20, 2021 - 11:52:02 AM
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Panagiotis Papadakis. A Use-Case Study on Multi-view Hypothesis Fusion for 3D Object Classification. ICCVW 2017 - IEEE International Conference on Computer Vision Workshop, Oct 2017, Venice, France. ⟨10.1109/ICCVW.2017.288⟩. ⟨hal-01699827⟩

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