Self-Supervised Monocular Depth Hints
Open accessAbstract
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser-scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground-truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth-prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth-suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.
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[27] uses a different split of the data, which is not the same as the Eigen split.
“[27] uses a different split of the data, which is not the same as the Eigen split.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [27]
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Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
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The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
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Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem
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Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
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GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes
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Learning Depth Estimation for Transparent and Mirror Surfaces
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NTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces
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NTIRE 2025 Challenge on HR Depth From Images of Specular and Transparent Surfaces
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Self-Supervised Deep Monocular Depth Estimation With Ambiguity Boosting
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Booster: A Benchmark for Depth From Images of Specular and Transparent Surfaces
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A Survey on Self-Supervised Monocular Depth Estimation Based on Deep Neural Networks
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OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments
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Self-Supervised Monocular Depth Estimation With Frequency-Based Recurrent Refinement
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Real-Time Self-Supervised Monocular Depth Estimation Without GPU
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Single-Image Depth Prediction Makes Feature Matching Easier
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Joint Optimization of Depth and Ego-Motion for Intelligent Autonomous Vehicles
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Learning Occlusion-aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation
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Self-Supervised Monocular Depth Estimation With Geometric Prior and Pixel-Level Sensitivity
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Recurrent Multiscale Feature Modulation for Geometry Consistent Depth Learning
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Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
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- Authors
- Watson, J., Firman, M., Brostow, G., & Turmukhambetov, D.
- Affiliations
- University College London (GB)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 2162-2171
- OpenAlex
- W2973531541
- Cited by
- 275
- References
- 46
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 46 · 2023: 64 · 2022: 47 · 2021: 47 · 2020: 29 · 2019: 1
- FWCI
- 9.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- monocular depth estimation, self-supervised learning, depth discontinuities, thin structures, KITTI dataset
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