SimpleView++: Neighborhood Views for Point Cloud Classification

Existing multi-view-based point cloud classification methods only utilize multiple views of point clouds and discard the point clouds from further processing. Among these methods, the Simple View model demonstrates that features from six orthogonal perspective projections of a point cloud achieved comparable 3D classification. However, points on the local structures overlap in these projections resulting in the loss of structural information. Also, we found that the performance of Simple View degrades at lower projection resolutions. We propose the use of neighbor projections along with object projections to learn finer local structural information. In this paper, we introduce SimpleView++ to concatenate features from the combined orthogonal perspective projections at object and neighbor levels with encoded features from the point cloud. We evaluated Simple-View++ using ModelNet40 and ScanObjectNN benchmark datasets. Our idea to use neighbor views broadly applies to other existing view-based methods. For example, neighbor views improve the results of MVTN by 2% on the hardest variant of ScanObjectNN. Our experiments also validate that neighbor view features considerably enhance classification accuracy at lower resolutions.