1 research outputs found
Cross-Shape Graph Convolutional Networks
We present a method that processes 3D point clouds by performing graph
convolution operations across shapes. In this manner, point descriptors are
learned by allowing interaction and propagation of feature representations
within a shape collection. To enable this form of non-local, cross-shape graph
convolution, our method learns a pairwise point attention mechanism indicating
the degree of interaction between points on different shapes. Our method also
learns to create a graph over shapes of an input collection whose edges connect
shapes deemed as useful for performing cross-shape convolution. The edges are
also equipped with learned weights indicating the compatibility of each shape
pair for cross-shape convolution. Our experiments demonstrate that this
interaction and propagation of point representations across shapes make them
more discriminative. In particular, our results show significantly improved
performance for 3D point cloud semantic segmentation compared to conventional
approaches, especially in cases with the limited number of training examples