Abstract
M. Ing.
The process of extracting depth information from multiple two-dimensional images taken of the same scene is known as stereo vision. It is of central importance to the field of machine vision as it is a low level task required for many higher level applications. The past few decades has witnessed the development of hundreds of different stereo vision algorithms. This has made it difficult to classify and compare the various approaches to the problem.
In this research we provide an overview of the types of approaches that exist to solve the problem of stereo vision. We focus on a specific subset of algorithms, known as local stereo algorithms. Our goal is to critically analyse and compare a representative sample of local stereo algorithm in terms of both speed and accuracy. We also divide the algorithms into discrete interchangeable components and experiment to determine the effect that each of the alternative components has on an algorithm’s speed and accuracy. We investigate even further to quantify and analyse the effect of various design choices within specific algorithm components. Finally we assemble all of the knowledge gained through the experimentation to compose and optimise a novel algorithm.
The experimentation highlighted the fact that by far the most important component of a local stereo algorithm is the manner in which it aggregates matching costs. All of the top performing local stereo algorithms dynamically define the shape of the windows over which the matching costs are aggregated. This is done in a manner that aims to only include pixels in a window that is likely to be at the same depth as the depth of the centre pixel of the window. Since the depth is unknown, the cost aggregation techniques use colour and proximity information to best guess whether pixels are at the same depth when defining the shape of the aggregation windows.
Local stereo algorithms are usually less accurate than global methods but they are supposed to be faster and more parallelisable. These cost aggregation techniques result in very accurate depth estimates but unfortunately they are also very expensive computationally. We believe the focus of local stereo algorithm development should be speed. Using the experimental results we developed an algorithm that achieves accuracies in the same order of magnitude as the state-of-the-art algorithms while reducing the computation time by over 50%.