Monocular-Cues Based 3-D Reconstruction: A Comparative Review

Monocular-Cues Based 3-D Reconstruction: A Comparative Review

Sudheer Tumu (State University of New York-Albany, USA), Viswanath Avasarala (GE Global Research, USA), Sai Tejaswi Jonnalagadda (Hetero Med Solutions, India) and Prasad Wadekar (Mahindra Satyam, USA)
DOI: 10.4018/978-1-4666-0113-0.ch008
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Abstract

3-D reconstruction from images has traditionally focused on using multiple images. However, in recent years, some interesting breakthroughs have been made in constructing depth maps of images using monocular cues. This chapter summarizes the recent work in this evolving research domain. The chapter also presents results from an initial exploratory study based on fusing the 3-D reconstructions generated by two seminal monocular-cue based reconstruction algorithms. With new testing data, the fusion approach improved the 3-D estimation accuracy significantly as compared to the original approaches. The authors use the improved estimation accuracy produced by the fusion algorithm as a motivating evidence for future work: the use of non-parametric Bayesian regression for 3-D reconstruction.
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Background

The 3-D reconstruction problem can be formulated in multiple ways (Zhang, R., Tsai, P.-S., Cryer, J., & Shah, M., 1999). A common formulation requires the calculation of depth value at each coordinate. The depths can be expressed as the distance of the surface point to the camera or from any other standard surface like the x-y plane. Other ways to express 3-D structure is by using surface normals, surface gradients or surface slant and tilt angles. In addition to these quantitative approaches, 3-D reconstruction algorithms can simply generate geometric/qualitative labels that allow the organization of the image into a 3-D hierarchy.

In this section, we provide a high level review of previous work on 3-D reconstruction both using binocular or multiple views and monocular views.

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