Yunlong Zhan

Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?

Geometric constraints are shown to enforce scale consistency and remedy the scale ambiguity issue in self-supervised monocular depth estimation. Meanwhile, scale-invariant losses focus on learning relative depth, leading to accurate relative depth prediction. To combine the best of both worlds, we l…