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Various effective deep networks have been developed for analysis of 3D point clouds. One key step in these networks is to aggregate the features of orderless points into a compact representation for the cloud. As a typical order-invariant aggregation method, max-pooling has been widely applied. However, while enjoying simplicity and high efficiency, max-pooling does not fully exploit the feature information since it not only ignores the non-maximum elements in each feature dimension but also neglects the interactions between different dimensions. These drawbacks of max-pooling motivate us to explore advanced feature aggregation methods for 3D point cloud analysis. The desired advanced method should be capable of modeling richer information between the point features than max-pooling, and, at the same time, it can readily replace max-pooling without the need to modify other parts of the existing network architecture. To this end, this paper proposes a novel kernel-based feature aggregat ....

Deep Learning , Eature Aggregation , Point Cloud ,