# Self-Supervised Monocular Image Depth Learning and Confidence Estimation

arXiv: https://arxiv.org/abs/1803.05530 · HTML: https://mlsanity.com/paper/1803.05530/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge](https://mlsanity.com/paper/2205.08668/) (2022)
- [SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation](https://mlsanity.com/paper/2601.17657/) (2026)
- [RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects](https://mlsanity.com/paper/2307.15988/) (2023)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks. We propose a novel framework for depth estimation from monocular images with corresponding confidence in a self-supervised manner. A fully differential patch-based cost function is proposed by using the Zero-Mean Normalized Cross Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This approach greatly increases the accuracy and robustness of the depth learning. In addition, the proposed patch-based cost function can provide a 0 to 1 confidence, which is then used to supervise the training of a parallel network for confidence map learning and estimation. Evaluation on KITTI dataset shows that our method outperforms the state-of-the-art results.