Improving the Robustness of Point Convolution on k-Nearest Neighbor Neighborhoods with a Viewpoint-Invariant Coordinate Transform

Recently, there is significant interest in performing convolution over irregularly sampled point clouds. Point clouds are very different from raster images, in that one cannot have a regular sampling grid on point clouds, which makes robustness under irregular neighborhoods an important issue. Especially, the k-nearest neighbor (kNN) neighborhood presents challenges for generalization because the location of the neighbors can be very different between training and testing times. In order to improve the robustness to different neighborhood samplings, this paper proposes a novel viewpoint-invariant coordinate transform as the input to the weight-generating function for point convolution, in addition to the regular 3D coordinates. This allows us to feed the network with non-invariant, scale-invariant and scale+rotation-invariant coordinates simultaneously, so that the network can learn which to include in the convolution function automatically. Empirically, we demonstrate that this effectively improves the performance of point cloud convolutions on the SemanticKITTI and ScanNet datasets, as well as the robustness to significant test-time downsampling, which can substantially change the distance of neighbors in a kNN neighborhood. Experimentally, among pure point-based approaches, we achieve comparable semantic segmentation performance with a comparable point-based convolution framework KPConv on SemanticKITTI and ScanNet, yet is significantly more efficient by virtue of using a kNN neighborhood instead of an ϵ-ball.