GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D

TMLR Paper3234 Authors

Abstract:

We introduce a novel approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). Unlike the conventional method of random
masking, our technique utilizes a teacher-student model to focus on intricate areas within the data, guiding the model’s focus toward regions with higher geometric complexity. This strategy is grounded in the hypothesis that concentrating on harder patches yields a more
robust feature representation, as evidenced by the improved performance on downstream tasks. Our method also presents a feature-level knowledge distillation technique designed to guide the prediction of geometric complexity, which utilizes a comprehensive context from
feature-level information. Extensive experiments confirm our method’s superiority over State-Of-The-Art (SOTA) baselines, demonstrating marked improvements in classification, segmentation, and few-shot tasks.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Wei_Liu3

Submission Number: 3234