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A Use-Case Study on Multi-View Hypothesis Fusion for 3D Object Classification
AbstractObject 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.
Related Material
[pdf][bibtex]@InProceedings{Papadakis_2017_ICCV,
author = {Papadakis, Panagiotis},
title = {A Use-Case Study on Multi-View Hypothesis Fusion for 3D Object Classification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}
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