# On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

arXiv: https://arxiv.org/abs/1803.09719 · HTML: https://mlsanity.com/paper/1803.09719/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

## Built on by (most cited first, 18 of 18)

- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [MVSAnywhere: Zero-Shot Multi-View Stereo](https://mlsanity.com/paper/2503.22430/) (2025)
- [Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator](https://mlsanity.com/paper/2502.19204/) (2025)
- [A Learned Stereo Depth System for Robotic Manipulation in Homes](https://mlsanity.com/paper/2109.11644/) (2021)
- [Mask-ToF: Learning Microlens Masks for Flying Pixel Correction in Time-of-Flight Imaging](https://mlsanity.com/paper/2103.16693/) (2021)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition](https://mlsanity.com/paper/2212.01538/) (2022)
- [Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions](https://mlsanity.com/paper/2210.08291/) (2022)
- [HawkDrive: A Transformer-driven Visual Perception System for Autonomous Driving in Night Scene](https://mlsanity.com/paper/2404.04653/) (2024)
- [StereoVAE: A lightweight stereo-matching system using embedded GPUs](https://mlsanity.com/paper/2305.11566/) (2023)
- [LVIC: Multi-modality segmentation by Lifting Visual Info as Cue](https://mlsanity.com/paper/2403.05159/) (2024)
- [Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devices](https://mlsanity.com/paper/2405.11809/) (2024)
- [Efficient stereo matching on embedded GPUs with zero-means cross correlation](https://mlsanity.com/paper/2212.00476/) (2022)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Centimeter-Wave Free-Space Time-of-Flight Imaging](https://mlsanity.com/paper/2105.11606/) (2021)
- [A Hybrid mmWave and Camera System for Long-Range Depth Imaging](https://mlsanity.com/paper/2106.07856/) (2021)
- [Pixel-Optimization-Free Patch Attack on Stereo Depth Estimation](https://mlsanity.com/paper/2506.17632/) (2025)

## Abstract (arXiv)

We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large-a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving. We argue that the challenges of removing this gap are significant, owing to fundamental limitations of monocular vision. As a result, we focus our efforts on depth estimation by stereo. We propose a novel semi-supervised learning approach to training a deep stereo neural network, along with a novel architecture containing a machine-learned argmax layer and a custom runtime (that will be shared publicly) that enables a smaller version of our stereo DNN to run on an embedded GPU. Competitive results are shown on the KITTI 2015 stereo dataset. We also evaluate the recent progress of stereo algorithms by measuring the impact upon accuracy of various design criteria.