Fast Point Cloud Sampling Network

Abstract

• A simple but effective point cloud sampling framework adaptive for multiple resolutions; • The sampling efficiency has been greatly improved over most former methods; • Experiments confirm that our work outperforms existing sampling methods on reconstruction and recognition. The increasing number of points in 3D point clouds has brought great challenges for subsequent algorithm efficiencies. Down-sampling algorithms are adopted to simplify the data and accelerate the computation. Except the well-known random sampling and farthest distance sampling, some recent works have tried to learn a sampling pattern according to the downstream task, which helps generate sampled points by fully-connected networks with fixed output point numbers. In this condition, a progress-net structure covering all resolutions sampling networks or multiple separate sampling networks for different resolutions are required, which is inconvenient. In this work, we propose a novel learning-based point cloud sampling framework, named Fast point cloud sampling network (FPN), which drives initial randomly sampled points to better positions instead of generating coordinates. FPN can be used to sample points clouds to any resolution once trained by changing the number of initial randomly sampled points. Results on point cloud reconstruction and recognition confirm that FPN can reach state-of-the-art performances with much higher sampling efficiency than most existing sampling methods.