Scale-Invariant Monocular Depth Estimation via SSI Depth
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913390628175872 |
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| author | Miangoleh, S. Mahdi H. Reddy, Mahesh Aksoy, Yağız |
| author_facet | Miangoleh, S. Mahdi H. Reddy, Mahesh Aksoy, Yağız |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_09374 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scale-Invariant Monocular Depth Estimation via SSI Depth Miangoleh, S. Mahdi H. Reddy, Mahesh Aksoy, Yağız Computer Vision and Pattern Recognition I.4.5 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. |
| title | Scale-Invariant Monocular Depth Estimation via SSI Depth |
| topic | Computer Vision and Pattern Recognition I.4.5 |
| url | https://arxiv.org/abs/2406.09374 |