  Domain adaptation (DA) techniques help deep learning models generalize across
data shifts for point cloud semantic segmentation (PCSS). Test-time adaptation
(TTA) allows direct adaptation of a pre-trained model to unlabeled data during
inference stage without access to source data or additional training, avoiding
privacy issues and large computational resources. We address TTA for geospatial
PCSS by introducing three domain shift paradigms: photogrammetric to airborne
LiDAR, airborne to mobile LiDAR, and synthetic to mobile laser scanning. We
propose a TTA method that progressively updates batch normalization (BN)
statistics with each testing batch. Additionally, a self-supervised learning
module optimizes learnable BN affine parameters. Information maximization and
reliability-constrained pseudo-labeling improve prediction confidence and
supply supervisory signals. Experimental results show our method improves
classification accuracy by up to 20\% mIoU, outperforming other methods. For
photogrammetric (SensatUrban) to airborne (Hessigheim 3D) adaptation at the
inference stage, our method achieves 59.46\% mIoU and 85.97\% OA without
retraining or fine-turning.
