Multi-View Stereo Reconstruction Technique

Multi-View Stereo Reconstruction Technique

Peng Song (Nanyang Technological University, Singapore) and Xiaojun Wu (Harbin Institute of Technology Shenzhen, China)
Copyright: © 2013 |Pages: 17
DOI: 10.4018/978-1-4666-3994-2.ch009
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Abstract

3D modeling of complex objects is an important task of computer graphics and poses substantial difficulties to traditional synthetic modeling approaches. The multi-view stereo reconstruction technique, which tries to automatically acquire object models from multiple photographs, provides an attractive alternative. The whole reconstruction process of the multi-view stereo technique is introduced in this chapter, from camera calibration and image acquisition to various reconstruction algorithms. The shape from silhouette technique is also introduced since it provides a close shape approximation for many multi-view stereo algorithms. Various multi-view algorithms have been proposed, which can be mainly classified into four classes: 3D volumetric, surface evolution, feature extraction and expansion, and depth map based approaches. This chapter explains the underlying theory and pipeline of each class in detail and analyzes their major properties. Two published benchmarks that are used to qualitatively evaluate multi-view stereo algorithms are presented, along with the benchmark criteria and evaluation results.
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Camera Calibration

Camera calibration is the process of finding the true parameters of the camera that produced a given photograph or video. Camera calibration is the crucial step in obtaining an accurate model of a target object. The calibration approaches can be categorized into two groups: full-calibration and self-calibration. Full-calibration approaches (Yemeza, 2004; Park, 2005) assume that a calibration pattern with precisely known geometry is presented in all input images, and computes the camera parameters consistent with a set of correspondences between the features defining the chart and their observed image projections. While the self-calibration approaches (Hernandez, 2004; Eisert, 2000; Fitzgibbon, 1998) are proposed to reduce the necessary prior knowledge about the scene camera geometry only to a few internal and external constraints. In these approaches, the intrinsic camera parameters are often supposed to be known a priori. However, since they require complex optimization techniques which are slow and difficult to converge, their accuracy is not comparable to that of the fully-calibrated systems. In practice, many applications such as 3D digitization of cultural heritage prefer to fully-calibrated systems since maximum accuracy is a very crucial requirement while self-calibration approaches are preferred when no Euclidean information is available such as reconstruction of a large scale outdoor building.

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