Zi-Zhan Guo

Learning to Discriminate Scene Structures Makes Self-Supervised Depth Learning Scalable

Self-supervised monocular depth estimation is appealing for its potential to scale with unlabeled data, yet existing methods often degrade when trained on diverse scenes. This study finds that, under self-supervision, models tend to encode dataset-specific structural biases rather than transferable scene geometry, lead...