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By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
SOTA for 3D Point Cloud Matching on Faust (using extra training data)
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.