2017 IEEE International Conference on Computer Vision Workshop (ICCVW) (2017)
Oct. 22, 2017 to Oct. 29, 2017
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, multiview 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.
Robot sensing systems, Semantics, Feature extraction, Shape, Three-dimensional displays, Conferences
P. Papadakis, "A Use-Case Study on Multi-view Hypothesis Fusion for 3D Object Classification," 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), Venice, Italy, 2017, pp. 2446-2452.