# Calibrating Self-supervised Monocular Depth Estimation

arXiv: https://arxiv.org/abs/2009.07714 · HTML: https://mlsanity.com/paper/2009.07714/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes](https://mlsanity.com/paper/2207.07895/) (2022)
- [CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters](https://mlsanity.com/paper/2110.14347/) (2021)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage](https://mlsanity.com/paper/2210.03570/) (2022)
- [Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains](https://mlsanity.com/paper/2303.07662/) (2023)
- [MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.10806/) (2022)
- [Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion](https://mlsanity.com/paper/2304.08916/) (2023)

## Abstract (arXiv)

In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal. Whilst the networks achieve good performance, the often over-looked detail is that due to the inherent ambiguity of monocular vision they predict depth up to an unknown scaling factor. The scaling factor is then typically obtained from the LiDAR ground truth at test time, which severely limits practical applications of these methods. In this paper, we show that incorporating prior information about the camera configuration and the environment, we can remove the scale ambiguity and predict depth directly, still using the self-supervised formulation and not relying on any additional sensors.