Daniel Bardsley

This Google summer of code project provides another c++ framework for evaluating stereo correlation algorithms and also a useful template library to ease OpenCV programming. The goal of this project is to provide a library of efficient 3D computer vision routines for image and video processing. It includes routines for dense stereo matching, optical flow (motion) estimation, occlusion detection, and egomotion (3D self-motion) estimation.

• Provides symmetric dense stereo matching with occlusions, symmetric dense optical flow with occlusions, probabilistic 3D egomotion estimation.
• Designed as a template library.
• Uses adapters to be compatible with Matlab, OpenCV, and can be easily tailored to be used with any other image processing library. Example code is included for OpenCV and Matlab.
• Modular structure of Stereo and Optical flow code makes it easy to add new algorithms for local matching, global matching, pre- and post-processing.
• Well-documented code.

You can visit the project homepage and download source code here.

The Middlebury Stereo Vision homepage provides results and evaluation code for comparing dense two frame stereo correlation algorithms. As well as providing a wealth of standard datasets with ground truth data the top ranking correlation algorithms are compared and evaluated for weaknesses. Finally Middlebury provide a comprehensive c++ stereo matching framework for comparing newly implemented correlation algorithms against other state of the art solutions. The home page, data and framework combine to support the work presented in Daniel Scharstein and Richard Szeliski’s 2001 paper: “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms“. [pdf]

Middlebury provide the following useful material on their site:

• An on-line evaluation of current algorithms
• Many stereo datasets with ground-truth disparities
• Stereo correspondence software
• An on-line submission script that allows you to evaluate your stereo algorithm in their framework