  3D point cloud segmentation has received significant interest for its growing
applications. However, the generalization ability of models suffers in dynamic
scenarios due to the distribution shift between test and training data. To
promote robustness and adaptability across diverse scenarios, test-time
adaptation (TTA) has recently been introduced. Nevertheless, most existing TTA
methods are developed for images, and limited approaches applicable to point
clouds ignore the inherent hierarchical geometric structures in point cloud
streams, i.e., local (point-level), global (object-level), and temporal
(frame-level) structures. In this paper, we delve into TTA in 3D point cloud
segmentation and propose a novel Hierarchical Geometry Learning (HGL)
framework. HGL comprises three complementary modules from local, global to
temporal learning in a bottom-up manner.Technically, we first construct a local
geometry learning module for pseudo-label generation. Next, we build prototypes
from the global geometry perspective for pseudo-label fine-tuning. Furthermore,
we introduce a temporal consistency regularization module to mitigate negative
transfer. Extensive experiments on four datasets demonstrate the effectiveness
and superiority of our HGL. Remarkably, on the SynLiDAR to SemanticKITTI task,
HGL achieves an overall mIoU of 46.91\%, improving GIPSO by 3.0\% and
significantly reducing the required adaptation time by 80\%. The code is
available at https://github.com/tpzou/HGL.
