Learning local shape descriptors with view-based convolutional networks

Figure 1: We present a view-based convolutional network that produces local, point-based shape descriptors. The network is trained such that geometrically and semantically similar points across different 3D shapes are embedded close to each other in descriptor space (left). Our produced descriptors are quite generic — they can be used in a variety of shape analysis applications, including dense matching, prediction of human affordance regions, partial scan-to-shape matching, and shape segmentation (right).