DFF-Matcher: Robust cross-source registration with density-fused feature and bidirectional consensus matching

Abstract

Cross-source point cloud registration plays a pivotal role in enabling seamless 3D perception across heterogeneous sensors. However, this task remains highly challenging due to significant density variations, sensor-specific noise, and partial overlaps between heterogeneous sensors. To address these challenges, we propose DFF-Matcher, a robust framework that integrates density-robust feature learning and bidirectional consensus matching to bridge domain gaps across different sensors. Our approach introduces a density-fused feature module to handle significant point density variations and a self-attention enhanced matching strategy to ensure reliable correspondence estimation. This unified framework establishes a new paradigm for cross-source registration, achieving superior performance across diverse sensor modalities. Extensive experiments demonstrate significant improvements, including 25.4% higher feature matching recall and 22.2% greater registration recall on challenging Kinect-LiDAR datasets, while maintaining robust performance in both indoor and outdoor scenarios.