MGE-Net: Task-oriented Point Cloud Sampling based on Multi-scale Geometry Estimation

A large number of collaborative manufacturing tasks are directly performed on point clouds. With the growing size of point clouds, the computational demands of these tasks also increase. One possible solution is to sample the point clouds. The most commonly used sampling method is farthest point sampling, but it does not consider downstream tasks, often leading to sampling non-informative points for the tasks. With the development of neural networks, various methods have been proposed to sample point clouds in a task-oriented learning manner. However, most methods are based on generation rather than selecting a subset of point clouds. In this work, we propose a novel adaptive keypoint sampling method, called MGE-Net, that combines neural network-based learning with direct point selection based on multi-scale geometry estimation. In addition, we design a feature extraction module based on multi-scale attention graph convolution to provide accurate information for subsequent keypoint detection. Relying on the contribution of point clouds to the task, our framework aims to sample a subset of point clouds specifically optimized for downstream tasks. Both qualitative and quantitative experimental results demonstrate that our sampling method exhibits superior performance in common point cloud classification and segmentation tasks.