Methodologies for Evaluating Disparity Estimation Algorithms

Methodologies for Evaluating Disparity Estimation Algorithms

Ivan Cabezas (Universidad del Valle, Colombia) and Maria Trujillo (Universidad del Valle, Colombia)
DOI: 10.4018/978-1-4666-2672-0.ch010
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The use of disparity estimation algorithms is required in the 3D recovery process from stereo images. These algorithms tackle the correspondence problem by computing a disparity map. The accuracy assessment of a disparity estimation process has multiple applications such as comparing among different algorithms’ performance, tuning algorithm’s parameters within a particular context, and determining the impact of components, among others. Disparity estimation algorithms can be assessed by following an evaluation methodology. This chapter is dedicated to present and discuss methodologies for evaluating disparity estimation algorithms. The discussion begins with a review of the state-of-the-art. The constitutive components of a methodology are analysed. Finally, advantages and drawbacks of existing methodologies are presented.
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A 3D scene can be described as a set of points in space. It can be captured, from different viewpoints, by a stereo camera system generating a stereo image pair. The conjugate projections of a common scene point are captured into image planes in different image coordinates. These projections are corresponding points, and the shift or displacement between them is termed disparity (Hartley & Zisserman, 2003). Disparity is, in essence, a vector related corresponding points. Disparity values are used to recover the 3D information of the captured scene. If the disparity of a point is known, its original 3D position can be recovered by a triangulation process. In fact, the 3D information recovery process from a stereo image is an inverse and ill-posed problem, due to depth ambiguity, data instability and lack of information. A stereo image pair can be modified in such a way that the displacement between corresponding points becomes horizontal (Fusiello et al., 2000). Such modified stereo image pair is the input to disparity estimation algorithms, which produce as output an estimated disparity map. Nevertheless, even in horizontal parallax, there is still a lack of information about the correspondences between points. Such lack of information gives rise to the stereo correspondence problem. Moreover, the stereo correspondence problem involves two inherent problems: ambiguity and occlusion. The projections into image planes are ambiguous by nature, since different objects, at different depths, can generate equal projections into image planes. Moreover, multiple objects captured into a stereo image pair may have the same appearance. On the other hand, a point may lack of a corresponding point – be occluded – in the conjugate image plane. In fact, occlusion phenomena arise naturally in stereo images, but it is not known beforehand.

The assessment of disparity estimation is important since a small inaccuracy in an estimated disparity may produce a large error in the recovered 3D. The performance of disparity estimation algorithms can be assessed by following an evaluation methodology. This assessment has several applications such as comparing algorithms (Szeliski, 1999; Szeliski & Zabih, 2000; Scharstein & Szeliski, 2002; Tombari et al., 2010; Cabezas & Trujillo, 2011), comparing methods and procedures (Tombari et al. 2008; Hirschmuller & Scharstein, 2009; Bleyer & Chambon, 2010), tuning algorithm’s parameters within a particular context (Hoyos et al., 2011), and identifying algorithm’s advantages or drawbacks (Kostlivá et al., 2008), among others.

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