  We introduce a pioneering 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 complete-to-partial feature-level
knowledge distillation technique designed to guide the prediction of geometric
complexity utilizing 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, and
few-shot tasks.
