  Three-dimensional (3D) shape recognition has drawn much research attention in
the field of computer vision. The advances of deep learning encourage various
deep models for 3D feature representation. For point cloud and multi-view data,
two popular 3D data modalities, different models are proposed with remarkable
performance. However the relation between point cloud and views has been rarely
investigated. In this paper, we introduce Point-View Relation Network (PVRNet),
an effective network designed to well fuse the view features and the point
cloud feature with a proposed relation score module. More specifically, based
on the relation score module, the point-single-view fusion feature is first
extracted by fusing the point cloud feature and each single view feature with
point-singe-view relation, then the point-multi-view fusion feature is
extracted by fusing the point cloud feature and the features of different
number of views with point-multi-view relation. Finally, the point-single-view
fusion feature and point-multi-view fusion feature are further combined
together to achieve a unified representation for a 3D shape. Our proposed
PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and
retrieval. Experimental results indicate our model can achieve significant
performance improvement compared with the state-of-the-art models.
