CVPR2025
PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation
Zidong Cao, Jinjing Zhu, Weiming Zhang, Hao Ai, Haotian Bai, Hengshuang Zhao, Lin Wang
Abstract
Rotation 20°10°( b) (a) Figure 1. (a) Our PanDA exhibits impressive panoramic depth estimation results in open-world scenarios. The resolution of presented panoramas is 1008×2016. (b) Top row: Spherical images with different zoom levels, and the corresponding depth predictions with perspective projection. Middle row: ERP image. Bottom row: Spherical images with different vertical rotation angles, and the corresponding depth predictions with perspective projection. Our PanDA is robust to spherical transformations and predicts fine-grained depths.