Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference
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The proposed model achieves state-of-the-art monocular and stereo self-supervised depth estimation results on the KITTI dataset.
“Together, these contributions yield state-of-the-art monocular and stereo self-supervised depth estimation results on the KITTI dataset [13], and simplify many components found in the existing top performing models.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [13]
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- Authors
- Li, B., Dai, Y., & He, M.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Dai, Yuchao
- He, Mingyi
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 83
- Pages
- 328-339
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2964193874
- Cited by
- 142
- References
- 45
- Citations by year
- 2025: 7 · 2024: 12 · 2023: 12 · 2022: 18 · 2021: 21 · 2020: 31 · 2019: 33 · 2018: 8
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Science Foundation of China (61420106007, 61671387)
- Australian Research Council (DE140100180)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- monocular depth estimation, dilated convolutional neural network, hierarchical feature fusion, quantization error reduction, semantic segmentation
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