APCoTTA introduces a continual test-time adaptation method for ALS point cloud semantic segmentation using gradient-driven layer selection, entropy-based consistency loss, and random parameter interpolation, with new benchmarks showing mIoU gains of 9-14%.
Curved build- ings reconstruction from airborne lidar data by matching and deforming geometric primitives
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APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds
APCoTTA introduces a continual test-time adaptation method for ALS point cloud semantic segmentation using gradient-driven layer selection, entropy-based consistency loss, and random parameter interpolation, with new benchmarks showing mIoU gains of 9-14%.