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Chapter 2 Previous Work 2.1 Perception of 3-d Objects from 2-d Data 2.1.1 Methods Using Specific Models
| Content Provider | Semantic Scholar |
|---|---|
| Abstract | Various methods have been proposed to infer buildings in particular, and 3-D objects in general, from 2-D data. Work using perceptual grouping techniques is discussed in some detail as it is an important technique used by many building detection and generic object recognition systems. With regard to choice of suitable model(s), most building detection systems use simple generic models such as rectangular parallelepipeds. In generic 3D object recognition some methods use specific models, while others use generic models. Some methods use the techniques of shading [32] or texture [89] to recover the 3-D information. However, a number of arguably unreasonable assumptions have to be made to enable these techniques to be applicable and effective. There are many methods that generate 3-D shape description from 3-D range data [87]; these methods are not directly related to the problem addressed here. In this chapter, work related to this dissertation is discussed. The chapter briefly covers work on the generic problem of perception of 3-D objects from one or more intensity images in Section 2.1. Section 2.2 details the work done in building detection. Perception of 3-D objects from intensity images is a hard problem. A number of general techniques have been developed. These are detailed in the following subsections. However, these techniques are either not applicable to the building detection (such as methods used on perfect contours, or those using specific models), or are too general to take advantage of domain-specific knowledge, which would result in poor performance when compared to similar systems designed to look specifically for buildings. Thus, while work 16 in generic 3-D object recognition provides many general insights into the problem of building detection, it is not sufficient to solve the problem. It is hard to detect 3-D objects from the lines extracted from one or more real intensity images, owing to fragmented low-level descriptions and background clutter. The use of geometric models of the objects helps to solve the segmentation problem as well as the 3-D inference problem. These may be specific models or generic models. A specific model represents a particular object, while a generic model represents a class of objects. This section discusses methods using specific geometric models. Roberts [78] uses a polyhedral-model matching technique to do the segmentation of the scene, which consists of polyhedral objects with homogeneous surfaces and uniform backgrounds. The ACRONYM system developed by Brooks uses specific models to |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://iris.usc.edu/Outlines/papers/1999/noronha_thesis/Chapter2.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Chapter |
