球面カーネル点畳み込みと深部中心投票によるSim2Real 3D物体分類
Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme
物体表面をグラフ表現し球面カーネル点畳み込みと投票機構で分類する手法を提案し、人工3Dモデルから実物体への転移精度を最大36%改善した。
著者: Jean-Baptiste Weibel, Timothy Patten, Markus Vincze
分類: cs.CV, cs.RO
原文アブストラクト
While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of annotation necessary to approach this problem, but most methods still struggle with the differences existing between artificial and real 3D data. We conjecture that the cause of those issue is the fact that many methods learn directly from point coordinates, instead of the shape, as the former is hard to center and to scale under variable occlusions reliably. We introduce spherical kernel point convolutions that directly exploit the object surface, represented as a graph, and a voting scheme to limit the impact of poor segmentation on the classification results. Our proposed approach improves upon state-of-the-art methods by up to 36% when transferring from artificial objects to real objects.
