Scale-Invariant Monocular Depth Estimation via SSI Depth
<|reference_start|>Scale-Invariant Monocular Depth Estimation via SSI Depth: 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.<|reference_end|>
@article{miangoleh2024scale-invariant,
    title={Scale-Invariant Monocular Depth Estimation via SSI Depth},
    author={S. Mahdi H. Miangoleh, Mahesh Reddy and Yau{g}{i}z Aksoy},
    journal={arXiv preprint arXiv:2406.09374},
    year={2024},
    doi={10.1145/3641519.3657523},
    archivePrefix={arXiv},
    eprint={2406.09374},
    primaryClass={cs.CV}
}