# Guided Stereo Matching

## TL;DR

This paper introduces Guided Stereo Matching, a method that uses sparse external depth cues to improve the accuracy and robustness of deep stereo networks and traditional algorithms, especially in new environments.

## Contribution

It proposes a general, differentiable framework that incorporates sparse depth measurements to enhance stereo matching performance and domain adaptability.

## Key findings

- Significant accuracy improvements with sparse cues on pre-trained networks
- Training from scratch increases robustness to domain shifts
- Effective even with traditional stereo algorithms like SGM

## Abstract

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.