Deep-learning Alignment for Handheld 3D Acquisitions: A new Densematch Dataset for an Extended Comparison

Abstract

Promising solutions for the alignment of 3D views based on representation learning approaches have been proposed very recently. The potentials of these solutions that could positively affect the 3D object registration has yet to be extensively tested. In fact, a direct comparison among advisable technologies is still lacking, especially if the focus is on different data types and real-time application requirements. This work is a first contribution in this direction since we perform an independent extended comparison among prominent deep learning-driven 3D view alignment solutions by considering two relevant setups: 1) data coming from commodity 3D sensors, and 2) denser data coming from a handheld 3D optical scanner. While for the first scenario reference datasets exist, we collect and release the new benchmark dataset DenseMatch for the second setup.