# Unsupervised Adversarial Depth Estimation using Cycled Generative Networks

arXiv: https://arxiv.org/abs/1807.10915 · HTML: https://mlsanity.com/paper/1807.10915/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2018.

## Built on by (most cited first, 30 of 45)

- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [Unsupervised Scale-consistent Depth Learning from Video](https://mlsanity.com/paper/2105.11610/) (2021)
- [Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth](https://mlsanity.com/paper/2203.15174/) (2022)
- [Learning Depth Estimation for Transparent and Mirror Surfaces](https://mlsanity.com/paper/2307.15052/) (2023)
- [Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions](https://mlsanity.com/paper/2407.16698/) (2024)
- [Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation](https://mlsanity.com/paper/2108.12545/) (2021)
- [Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images](https://mlsanity.com/paper/2107.04644/) (2021)
- [SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation](https://mlsanity.com/paper/2309.00526/) (2023)
- [DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2305.06225/) (2023)
- [Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey](https://mlsanity.com/paper/2406.19675/) (2024)
- [Generative Modeling for Multi-task Visual Learning](https://mlsanity.com/paper/2106.13409/) (2021)
- [The Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2211.12174/) (2022)
- [SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images](https://mlsanity.com/paper/2403.10662/) (2024)
- [H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry](https://mlsanity.com/paper/2104.11288/) (2021)
- [GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network](https://mlsanity.com/paper/2112.06782/) (2021)
- [Manydepth2: Motion-Aware Self-Supervised Monocular Depth Estimation in Dynamic Scenes](https://mlsanity.com/paper/2312.15268/) (2023)
- [Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues](https://mlsanity.com/paper/2102.03602/) (2021)
- [Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation](https://mlsanity.com/paper/2203.09737/) (2022)
- [Self-Supervised Learning of Domain Invariant Features for Depth Estimation](https://mlsanity.com/paper/2106.02594/) (2021)
- [Embodiment: Self-Supervised Depth Estimation Based on Camera Models](https://mlsanity.com/paper/2408.01565/) (2024)
- [Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction](https://mlsanity.com/paper/2101.02843/) (2021)
- [Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes](https://mlsanity.com/paper/2105.02195/) (2021)
- [Absolute distance prediction based on deep learning object detection and monocular depth estimation models](https://mlsanity.com/paper/2111.01715/) (2021)
- [DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume](https://mlsanity.com/paper/2308.07225/) (2023)
- [SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2404.12501/) (2024)
- [ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusion](https://mlsanity.com/paper/2407.09303/) (2024)
- [UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion](https://mlsanity.com/paper/2204.07791/) (2022)
- [Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry](https://mlsanity.com/paper/2207.06351/) (2022)
- [Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models](https://mlsanity.com/paper/2411.05005/) (2024)
- [How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting](https://mlsanity.com/paper/2207.00506/) (2022)

## Abstract (arXiv)

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 and trained model are available on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.