CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Open accessAbstract
Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model. Thus, changing the camera model requires collecting an entirely new training dataset. In this work, we propose a new type of convolution that can take the camera parameters into account, thus allowing neural networks to learn calibration-aware patterns. Experiments confirm that this improves the generalization capabilities of depth prediction networks considerably, and clearly outperforms the state of the art when the train and test images are acquired with different cameras.
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There is minimal work in the area of fisheye perception; specifically, the only previous work on multi-task learning is FisheyeMultiNet, which discusses a more straightforward three-task network.
“In general, there is minimal work in the area of fisheye perception.Specifically, there is only our previous work on multi-task learning: FisheyeMultiNet [6] which discusses a more straight forward three task network.”
From OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving (2021) · cites this paper as [6]
The Related Work section discusses single-view depth prediction and networks that jointly predict depth and segmentation, but does not address how much work exists on fisheye perception. Searches for “fisheye,” “FisheyeMultiNet,” and fisheye multi-task learning found no passage identifying FisheyeMultiNet or describing a three-task network. (LLM, conf=0.76)
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Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
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Input-level Inductive Biases for 3D Reconstruction
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3D Reconstruction of Novel Object Shapes from Single Images
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Deep Learning-Based Stereopsis and Monocular Depth Estimation Techniques: A Review
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MonoEdge: Monocular 3D Object Detection Using Local Perspectives
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Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation
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Autonomous Detection and Deterrence of Pigeons on Buildings by Drones
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LightDepth: Single-View Depth Self-Supervision from Illumination Decline
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Foggy Lane Dataset Synthesized from Monocular Images for Lane Detection Algorithms
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Aberration-Aware Depth-From-Focus
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Lightweight Monocular Depth with a Novel Neural Architecture Search Method
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Instant Visual Odometry Initialization for Mobile AR
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An Overview of Perception and Decision-Making in Autonomous Systems in the Era of LearningPreprint
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Monocular Depth Estimation: A Survey
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SfM-TTR: Using Structure from Motion for Test-Time Refinement of Single-View Depth Networks
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Self-citation3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces
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Self-citationThe Second Monocular Depth Estimation Challenge
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NoVA: Learning to See in Novel Viewpoints and Domains
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NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
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SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens
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AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration
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Self-citationDeep Learning based Monocular Depth Prediction: Datasets, Methods and ApplicationsPreprint
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Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation
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Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
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A Review of Benchmark Datasets and Training Loss Functions in Neural Depth Estimation
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- Authors
- Facil, J. M., Ummenhofer, B., Zhou, H., Montesano, L., Brox, T., & Civera, J.
- Affiliations
- Univ. of Zaragoza
- Intel (United States) (US)
- University of Freiburg (DE)
- Universidad de Zaragoza (ES)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 11818-11827
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2928601293
- Cited by
- 130
- References
- 54
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 15 · 2023: 25 · 2022: 20 · 2021: 35 · 2020: 14 · 2019: 4
- FWCI
- 5.95
- Type
- conference paper
- Language
- en
- Open access
- green
- zaguan.unizar.es
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Zaguan (University of Zaragoza Repository) (submittedVersion, other-oa)
- Funders
- Nvidia
- Fundación Caja Inmaculada
- 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
- single-view depth estimation, multi-scale convolution, camera calibration
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