Methods of 3D Object Shape Acquisition

Methods of 3D Object Shape Acquisition

Pavel Zemcik (Brno University of Technology, Czech Republic), Michal Spanel (Brno University of Technology, Czech Republic), Premysl Krsek (Brno University of Technology, Czech Republic) and Miloslav Richter (Brno University of Technology, Czech Republic)
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-3994-2.ch024
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Abstract

This chapter contains an overview of methods for a 3D object shape from both the surface and the internal structure of the objects. The acquisition methods of interest are optical methods based on objects surface image processing and CT/NMR sensors that explore the object volume structure. The chapter also describes some methods for 3D shape processing. The focus is on 3D surface shape acquisition methods based on multiple views, methods using single view video sequences, and methods that use a single view with a controlled light source. In addition, the volume methods represented by CT/NMR are covered as well. A set of algorithms suitable for the acquired 3D data processing and simplification are shown to demonstrate how the models data can be processed. Finally, the chapter discusses future directions and then draws conclusions.
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Background

The recognition of 3D shapes through measurement of the existing scenes and through processing of sensory information is a complex task. Description of all the approaches used and attempted would be beyond the scope of this text. The main approaches used today are overviewed and explained here. Nowadays, the main approaches include:

  • Getting 3D coordinates from images or video of the scene – this approach is interesting as it is intended for acquisition of 3D scenes based on image and video information only without any other source of (sensory) information. Therefore, it can also be seen as means of acquisition of the 3D scene from image data sensors that are generally available and that exploit arbitrary data (Kraus, 2000; Koch, 1995; Pollefeys et al., 1998).

  • Obtaining 3D data from specialized image sensors – the approach based on images but using specialized light sources. While this approach requires specialized sensor setups, it might be simpler, less expensive and also more precise compared to the above methods (Zhang, 2005).

  • Processing other than image sensors to get 3D data – a typical example of such an approach can be seen in medical imaging where 3D models of tissues are obtained through CT and/or NMR data that are in their nature not image data but still can carry information about the 3D scene (Vivodtzev et al., 2003; Du & Wang, 2003; Labelle & Shewchuk, 2007).

  • Other methods of getting 3D data - such as measurement of the scenes through various distance measurement devices, fusion of the information from different types of sensors, etc. These approaches are beyond the scope of this text.

The data obtained through the methods mentioned above do not necessarily fulfill the requirements of the application for which it is intended. Therefore, postprocessing of the data often needs to be done. The postprocessing can include conversion of the data representation, reduction of data size, ensuring integrity of the data, etc. This text outlines an overview of the approaches in order to give the reader further insight in this area.

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