# DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos

arXiv: https://arxiv.org/abs/2003.01360 · HTML: https://mlsanity.com/paper/2003.01360/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimation](https://mlsanity.com/paper/2102.03550/) (2021)
- [EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation](https://mlsanity.com/paper/2304.03369/) (2023)
- [PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation](https://mlsanity.com/paper/2110.05839/) (2021)
- [X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation](https://mlsanity.com/paper/2110.12516/) (2021)
- [DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction](https://mlsanity.com/paper/2303.01573/) (2023)
- [Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation](https://mlsanity.com/paper/2407.04041/) (2024)
- [Unsupervised Monocular Depth Perception: Focusing on Moving Objects](https://mlsanity.com/paper/2108.13062/) (2021)
- [EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity](https://mlsanity.com/paper/2309.01296/) (2023)
- [Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2309.05254/) (2023)
- [Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes](https://mlsanity.com/paper/2303.14628/) (2023)

## Abstract (arXiv)

Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one. It achieves this by using the photometric errors between the target view and the synthesized views from its adjacent source views as the loss. Despite significant progress, the learning still suffers from occlusion and scene dynamics. This paper shows that carefully manipulating photometric errors can tackle these difficulties better. The primary improvement is achieved by a statistical technique that can mask out the invisible or nonstationary pixels in the photometric error map and thus prevents misleading the networks. With this outlier masking approach, the depth of objects moving in the opposite direction to the camera can be estimated more accurately. To the best of our knowledge, such scenarios have not been seriously considered in the previous works, even though they pose a higher risk in applications like autonomous driving. We also propose an efficient weighted multi-scale scheme to reduce the artifacts in the predicted depth maps. Extensive experiments on the KITTI dataset show the effectiveness of the proposed approaches. The overall system achieves state-of-theart performance on both depth and ego-motion estimation.