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

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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Dates and versions

hal-01699827 , version 1 (02-02-2018)

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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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