A Survey on Deep Learning Techniques for Stereo-based Depth Estimation

TL;DR

This survey reviews deep learning methods for stereo-based depth estimation, highlighting their advancements, challenges, and future directions in applications like autonomous driving and augmented reality.

Contribution

It provides a comprehensive overview of deep learning techniques for stereo depth estimation, summarizing common pipelines, benefits, limitations, and future prospects.

Findings

Deep learning has significantly improved stereo depth estimation performance.

Traditional methods struggle with textureless regions and occlusions.

Deep learning enables real-world applications like autonomous vehicles.

Abstract

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this article, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research.