A Novel Street View MVS Pipeline with Edge-Enhanced Sky Masking and Cross Algorithm Data Fusion

Abstract. MVS (Multi-View Stereo) establishes dense correspondences among multiple calibrated images to generate 3D point clouds, which has broad applications in fields such as 3D modeling, robot navigation, and autonomous driving. In street view MVS, the distant, weakly-textured sky pixels in images significantly degrade the quality of the generated point cloud, manifesting as pronounced edge noise at building boundaries, and the completeness of the point cloud requires further improvement. Therefore, we design an Edge-enhanced Sky Masking Module to free street view MVS from sky interference, reducing edge noise by approximately 40%. In addition, we propose a Fusion Module based on Local Planarity Features, which integrates the strengths of both traditional and learning-based algorithms to generate superior dense point clouds, outperforming current mainstream methods in terms of completeness and F1 score.