  Existing methods for scale-invariant monocular depth estimation (SI MDE)
often struggle due to the complexity of the task, and limited and non-diverse
datasets, hindering generalizability in real-world scenarios. This is while
shift-and-scale-invariant (SSI) depth estimation, simplifying the task and
enabling training with abundant stereo datasets achieves high performance. We
present a novel approach that leverages SSI inputs to enhance SI depth
estimation, streamlining the network's role and facilitating in-the-wild
generalization for SI depth estimation while only using a synthetic dataset for
training. Emphasizing the generation of high-resolution details, we introduce a
novel sparse ordinal loss that substantially improves detail generation in SSI
MDE, addressing critical limitations in existing approaches. Through
in-the-wild qualitative examples and zero-shot evaluation we substantiate the
practical utility of our approach in computational photography applications,
showcasing its ability to generate highly detailed SI depth maps and achieve
generalization in diverse scenarios.
