Starting from Non-Parametric Networks for 3D Point Cloud Analysis

Renrui Zhang

Abstract: We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions. Surprisingly, it performs well on various 3D tasks, requiring no parameters or training, especially for few-shot classification. Starting from this basic non-parametric model, we propose two extensions. First, Point-NN can serve as a base architectural framework to construct Parametric Networks by simply inserting linear layers on top. Given the superior non-parametric foundation, the derived Point-PN exhibits a high performance-efficiency trade-off with only a few learnable parameters. Second, Point-NN can be regarded as a plug-and-play module to enhance the already trained 3D models without re-training. Point-NN captures the complementary geometric knowledge and boosts existing methods for different 3D benchmarks by interpolating the predictions during inference. We conduct extensive experiments to revisit the non-parametric network components and demonstrate their significance for 3D point cloud understanding.