Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration
This work addresses the problem of efficient and accurate point cloud registration for the computer vision community, presenting an incremental improvement through a graph neural network reformulation.
The paper tackles the challenge of learning from sparse 3D point clouds by introducing Graphite, a graph-induced feature extraction pipeline for keypoint detection and description, achieving comparable results to state-of-the-art methods on registration benchmarks with processing times of ~0.018 seconds for 100 patches.
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community. However, due to the sparsity of their representation, learning models based on large point clouds is still a challenge. In this work, we introduce Graphite, a GRAPH-Induced feaTure Extraction pipeline, a simple yet powerful feature transform and keypoint detector. Graphite enables intensive down-sampling of point clouds with keypoint detection accompanied by a descriptor. We construct a generic graph-based learning scheme to describe point cloud regions and extract salient points. To this end, we take advantage of 6D pose information and metric learning to learn robust descriptions and keypoints across different scans. We Reformulate the 3D keypoint pipeline with graph neural networks which allow efficient processing of the point set while boosting its descriptive power which ultimately results in more accurate 3D registrations. We demonstrate our lightweight descriptor on common 3D descriptor matching and point cloud registration benchmarks and achieve comparable results with the state of the art. Describing 100 patches of a point cloud and detecting their keypoints takes only ~0.018 seconds with our proposed network.