Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks
Open accessAbstract
While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field (i.e. the disparity map) between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code is available at https://github.com/andrea-pilzer/unsup-stereo-depthGAN.
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Our models generate higher quality outputs and do not produce holes in the depth maps or border artifacts that can be seen in many existing baselines.
“We can see that our models generate higher quality outputs and do not produce 'holes' in the depth maps or border artifacts that can be seen in many existing baselines e.g.[76,51,15,73].”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [73]
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Adversarial Learning for Joint Optimization of Depth and Ego-Motion
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A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation
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Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
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Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data
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Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
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H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry
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Adaptive Co-teaching for Unsupervised Monocular Depth Estimation
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Occlusion-Aware Unsupervised Light Field Depth Estimation Based on Multi-Scale GANs
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Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
Xu, D., Alameda-Pineda, X., Ouyang, W., Ricci, E., Wang, X., & Sebe, N. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3043781 · Cited by 24 · see citing works
Self-citationSelf-supervised monocular depth estimation in dynamic scenes with moving instance loss
Yue, M., Fu, G., Wu, M., Zhang, X., & Gu, H. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104862 · Cited by 22 · see citing works
Constant Velocity Constraints for Self-Supervised Monocular Depth Estimation
Zhou, H., Greenwood, D., Taylor, S. L., & Gong, H. (2020) · Conference on Visual Media Production · doi:10.1145/3429341.3429355 · Cited by 21 · see citing works
Self-Supervised Learning of Domain Invariant Features for Depth Estimation
Akada, H., Bhat, S. F., Alhashim, I., & Wonka, P. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00107 · Cited by 20 · see citing works
DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Hong, F.-T., Shen, L., & Xu, D. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3339964 · Cited by 22 · see citing works
Self-citationManydepth2: Motion-Aware Self-Supervised Monocular Depth Estimation in Dynamic Scenes
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Simultaneous Semantic Segmentation and Depth Completion with Constraint of Boundary
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Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks
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Unsupervised framework for depth estimation and camera motion prediction from video
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When Visual Disparity Generation Meets Semantic Segmentation: A Mutual Encouragement Approach
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Unsupervised Learning of Depth Estimation and Camera Pose With Multi-Scale GANs
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SPIdepth: Strengthened Pose Information for Self-Supervised Monocular Depth Estimation
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DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES
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Binocular Visual Measurement Method Based on Feature Matching
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Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes
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Self-citationAn Overview of Perception and Decision-Making in Autonomous Systems in the Era of LearningPreprint
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UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion
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SLAM Based on Camera-2D LiDAR Fusion
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Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation
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Self-supervised coarse-to-fine monocular depth estimation using a lightweight attention module
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Deep Quantified Visibility Estimation for Traffic Image
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IterGANs: Iterative GANs to learn and control 3D object transformation
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Joint prediction of monocular depth and structure using planar and parallax geometry
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- Authors
- Pilzer, A., Xu, D., Puscas, M., Ricci, E., & Sebe, N.
- Affiliations
- University of Trento (IT)
- University of Oxford (GB)
- Venue
- 2018 International Conference on 3D Vision (3DV)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 587-595
- OpenAlex
- W2884609081
- Cited by
- 192
- References
- 32
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 22 · 2023: 17 · 2022: 37 · 2021: 36 · 2020: 32 · 2019: 29 · 2018: 2
- FWCI
- 7.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on 3D Vision (3DV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Iris (University of Trento) (submittedVersion)
- arXiv (Cornell University)
- 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 › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- unsupervised depth estimation, monocular depth estimation, adversarial learning, generative adversarial networks, cycle consistency, stereo matching, disparity map, depth map estimation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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