Point Cloud Denoising in Outdoor Real-World Scenes Based on Measurable Segmentation

Abstract

With the continuous advancements in three-dimensional scanning technology, point clouds are fundamental data in various fields such as autonomous driving, 3D urban modeling, and the preservation of cultural heritage. However, inherent inaccuracies in instruments and external environmental interference often introduce noise and outliers into point cloud data, posing numerous challenges for advanced processing tasks such as registration, segmentation, classification, and 3D reconstruction. To effectively address these issues, this study proposes a hierarchical denoising strategy based on finite measurable segmentation in spherical space, taking into account the performance differences in horizontal and vertical resolutions of LiDAR systems. The effectiveness of this method was validated through a denoising experiment conducted on point cloud data collected from real outdoor environments. The experimental result indicates that this denoising strategy not only effectively eliminates noise but also more accurately preserves the original detail features of the point clouds, demonstrating significant advantages over conventional denoising techniques. Overall, this study introduces a novel and effective method for denoising point cloud data in outdoor real-world scenes.