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.

借助 SSI 输入来增强 SI 深度估计的新方法，通过引入一个新的稀疏排序损失，实现 SSI MDE 中高分辨率细节的生成，同时在计算摄影应用中展示了其生成高度详细的 SI 深度图并在多种情境中实现了泛化。