Trajectory Regularization Enhances Self-Supervised Geometric
  Representation
Jiayun Wang Stella X. Yu Yubei Chen
Self-supervised learning (SSL) has proven effective in learning high-quality
representations for various downstream tasks, with a primary focus on semantic
tasks. However, its application in geometric tasks remains underexplored,
partially due to the absence of a standardized evaluation method for geometric
representations. To address this gap, we introduce a new pose-estimation
benchmark for assessing SSL geometric representations, which demands training
without semantic or pose labels and achieving proficiency in both semantic and
geometric downstream tasks. On this benchmark, we study enhancing SSL geometric
representations without sacrificing semantic classification accuracy. We find
that leveraging mid-layer representations improves pose-estimation performance
by 10-20%. Further, we introduce an unsupervised trajectory-regularization
loss, which improves performance by an additional 4% and improves
generalization ability on out-of-distribution data. We hope the proposed
benchmark and methods offer new insights and improvements in self-supervised
geometric representation learning.