# Unsupervised High-Resolution Depth Learning From Videos With Dual Networks

arXiv: https://arxiv.org/abs/1910.08897 · HTML: https://mlsanity.com/paper/1910.08897/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 26 of 26)

- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2112.13047/) (2021)
- [Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2108.08829/) (2021)
- [Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2109.12484/) (2021)
- [Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth](https://mlsanity.com/paper/2203.15174/) (2022)
- [Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry](https://mlsanity.com/paper/2112.08177/) (2021)
- [Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation](https://mlsanity.com/paper/2108.07628/) (2021)
- [Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion](https://mlsanity.com/paper/2103.16690/) (2021)
- [PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss](https://mlsanity.com/paper/2103.07362/) (2021)
- [MGNet: Monocular Geometric Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2206.13199/) (2022)
- [S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation](https://mlsanity.com/paper/2104.00877/) (2021)
- [DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction](https://mlsanity.com/paper/2209.06351/) (2022)
- [URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation](https://mlsanity.com/paper/2302.08149/) (2023)
- [Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR](https://mlsanity.com/paper/2109.09628/) (2021)
- [GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network](https://mlsanity.com/paper/2112.06782/) (2021)
- [Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos](https://mlsanity.com/paper/2106.03505/) (2021)
- [Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2203.10925/) (2022)
- [Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance](https://mlsanity.com/paper/2304.05716/) (2023)
- [Polarimetric Imaging for Perception](https://mlsanity.com/paper/2305.14787/) (2023)
- [Fractal Pyramid Networks](https://mlsanity.com/paper/2106.14694/) (2021)
- [On Robust Cross-View Consistency in Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2209.08747/) (2022)
- [CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose](https://mlsanity.com/paper/2212.05845/) (2022)
- [Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning](https://mlsanity.com/paper/2205.10006/) (2022)
- [Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera](https://mlsanity.com/paper/2306.11822/) (2023)
- [DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos](https://mlsanity.com/paper/2403.05895/) (2024)
- [Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2604.07665/) (2026)

## Abstract (arXiv)

Unsupervised depth learning takes the appearance difference between a target view and a view synthesized from its adjacent frame as supervisory signal. Since the supervisory signal only comes from images themselves, the resolution of training data significantly impacts the performance. High-resolution images contain more fine-grained details and provide more accurate supervisory signal. However, due to the limitation of memory and computation power, the original images are typically down-sampled during training, which suffers heavy loss of details and disparity accuracy. In order to fully explore the information contained in high-resolution data, we propose a simple yet effective dual networks architecture, which can directly take high-resolution images as input and generate high-resolution and high-accuracy depth map efficiently. We also propose a Self-assembled Attention (SA-Attention) module to handle low-texture region. The evaluation on the benchmark KITTI and Make3D datasets demonstrates that our method achieves state-of-the-art results in the monocular depth estimation task.