Mandal, Anil Kumar

This research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the pipeline’s robustness in handling datasets with up to 49.73 million points. The models achieved average epoch times of 35 seconds for DeepGMR, 112 seconds for FMR, and 333 seconds for PointNetLK. Accuracy in alignment was also reliable, with rotation errors averaging 2.955, 1.966, and 1.918 degrees, and translation errors at 0.174, 0.191, and 0.175 meters, respectively. This scalable, high-performance pipeline offers a practical solution for spatial data processing, making it suitable for applications that require precise alignment in large, cross-source datasets, such as mapping, urban planning, and environmental analysis.