This repository is a curated list of state-of-the-art deep stereo matching resources, including papers, codebases, datasets, and tutorials. It serves as a comprehensive reference for researchers and practitioners in computer vision and robotics interested in depth estimation from stereo imagery. The project aims to consolidate the rapidly evolving field of deep stereo matching, providing a structured overview of key advancements and methodologies.

How It Works

The repository categorizes deep stereo matching literature into thematic sections such as Foundational Architectures, Efficient Architectures, Multi-Task Learning, Beyond Visual Spectrum, Architectural Analysis, and Challenges & Solutions. Each entry typically includes links to the original paper, associated code repositories, and relevant datasets, facilitating easy access to the underlying research. Groundbreaking works are highlighted with a specific flag.

Quick Start & Requirements

This repository is a curated list and does not involve direct installation or execution. Users can navigate the categorized links to access research papers, code, and datasets. Requirements vary per linked resource, typically involving Python environments, deep learning frameworks (PyTorch, TensorFlow), and specific hardware (GPUs) for running models.

Highlighted Details

• Comprehensive Coverage: Encompasses a wide range of deep stereo matching techniques, from foundational CNNs to Transformer-based and event-based approaches.
• Extensive Dataset Catalog: Lists numerous real-world and synthetic datasets crucial for training and evaluating stereo matching models.
• Associated Surveys: Links to influential survey papers by the maintainers, providing in-depth overviews and context for the listed resources.
• Regular Updates: The "in the Twenties" survey and the repository's structure suggest an effort to keep pace with recent advancements.

Maintenance & Community

The repository is maintained by Fabio Tosi, Matteo Poggi, and Luca Bartolomei from the University of Bologna, authors of several key survey papers in the field. Contributions are welcomed via pull requests.

Licensing & Compatibility

The repository itself is a list of links and does not have a specific license. The licensing of individual linked papers, code, and datasets varies and must be checked on their respective sources.

Limitations & Caveats

As a curated list, the repository's value is dependent on the accuracy and completeness of its links. While extensive, it may not cover every single publication in the rapidly advancing field of deep stereo matching.