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, and even surpasses existing fully trained models.
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 for the already trained 3D
models during inference. Point-NN captures the complementary geometric
knowledge and enhances existing methods for different 3D benchmarks without
re-training. We hope our work may cast a light on the community for
understanding 3D point clouds with non-parametric methods. Code is available at
this https URL.

我们提出了一种非参数网络，Point-NN，用于 3D 点云分析，可以作为构建 Parametric Networks 的基本框架，也可作为已训练的 3D 模型的插拔式模块，旨在为社区提供了解非参数方法处理 3D 点云的帮助。