  Understanding point clouds captured from the real-world is challenging due to
shifts in data distribution caused by varying object scales, sensor angles, and
self-occlusion. Prior works have addressed this issue by combining recent
learning principles such as self-supervised learning, self-training, and
adversarial training, which leads to significant computational overhead.Toward
succinct yet powerful domain adaptation for point clouds, we revisit the unique
challenges of point cloud data under domain shift scenarios and discover the
importance of the global geometry of source data and trends of target
pseudo-labels biased to the source label distribution. Motivated by our
observations, we propose an adapter-guided domain adaptation method,
PC-Adapter, that preserves the global shape information of the source domain
using an attention-based adapter, while learning the local characteristics of
the target domain via another adapter equipped with graph convolution.
Additionally, we propose a novel pseudo-labeling strategy resilient to the
classifier bias by adjusting confidence scores using their class-wise
confidence distributions to consider relative confidences. Our method
demonstrates superiority over baselines on various domain shift settings in
benchmark datasets - PointDA, GraspNetPC, and PointSegDA.
