  3D point clouds captured from real-world sensors frequently encompass noisy
points due to various obstacles, such as occlusion, limited resolution, and
variations in scale. These challenges hinder the deployment of pre-trained
point cloud recognition models trained on clean point clouds, leading to
significant performance degradation. While test-time adaptation (TTA)
strategies have shown promising results on this issue in the 2D domain, their
application to 3D point clouds remains under-explored. Among TTA methods, an
input adaptation approach, which directly converts test instances to the source
domain using a pre-trained diffusion model, has been proposed in the 2D domain.
Despite its robust TTA performance in practical situations, naively adopting
this into the 3D domain may be suboptimal due to the neglect of inherent
properties of point clouds, and its prohibitive computational cost. Motivated
by these limitations, we propose CloudFixer, a test-time input adaptation
method tailored for 3D point clouds, employing a pre-trained diffusion model.
Specifically, CloudFixer optimizes geometric transformation parameters with
carefully designed objectives that leverage the geometric properties of point
clouds. We also substantially improve computational efficiency by avoiding
backpropagation through the diffusion model and a prohibitive generation
process. Furthermore, we propose an online model adaptation strategy by
aligning the original model prediction with that of the adapted input.
Extensive experiments showcase the superiority of CloudFixer over various TTA
baselines, excelling in handling common corruptions and natural distribution
shifts across diverse real-world scenarios. Our code is available at
https://github.com/shimazing/CloudFixer
