An Adaptive Point Cloud Downsampling Method for Large-Scale Outdoor LiDAR Point Cloud Registration

One of the characteristics of outdoor scene point clouds is their large quantity, so it demands substantial computational resources for processing. Sampling thus plays a critical role in efficient processing. Most existing methods overlook scene and task-specific characteristics, relying solely on global point distribution. To address this, we propose an adaptive downsampling strategy for large-scale outdoor light detection and ranging (LiDAR) point cloud registration. By statistically analyzing semantic labels, we separate foreground and background point clouds, recognizing that background categories may vary across scenes. We then sample high-curvature points from the background and contour points from the foreground to preserve discriminative spatial distribution features. Extensive experiments on outdoor datasets demonstrate that our method achieves comparable performance to state-of-the-art methods.