Paul J. Besl
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Abstract
A general-purpose computer vision system must be capable of recognizing three-dimensional (3-D) objects. This paper proposes a precise definition of the 3-D object recognition problem, discusses basic concepts associated with this problem, and reviews the relevant literature. Because range images (or depth maps) are often used as sensor input instead of intensity images, techniques for obtaining, processing, and characterizing range data are also surveyed.
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Leon A. Pintsov
This paper is a survey of mostly recent literature dedicated to the autonomous single arbitrary view, 3D object-recognition problem. The first two sections are concerned with problem definition and mathematical problem formulation. The brief summary of the problem definition is as follows. Given: (1) a collection of labeled solid objects, (2) digitized sensor data corresponding to one arbitrary field of view of the real world, and (3) the list of distinguishable objects, then, for every object from the list, the number of its occurrences in the sensor data along with their 3D locations and orientations (with respect to known coordinate system) have to be found. The mathematical definition of this problem is given for the range and intensity images. In the next two sections, general structure of the object recognition system and characteristics of an ideal system are explained. The largest section of the paper is the literature review, and it is divided into the following subject areas: (1)3D object-representation schemes (2)3D surface-representation schemes (3)3D object- and surface-rendering algorithms (4)Intensity and range image formation (5)Intensity and range image processing (6)3D surface characterization (7)3D object reconstruction algorithms (8)3D object recognition systems using intensity images (9)3D object recognition systems using range images The paper is concluded with several general observations presenting the authors' opinion of the subject and suggestions for the direction of the future research. The list of references contains 202 items. The paper is well written. It presents a wealth of material on the 3D object recognition problem. This paper is a good attempt to develop a consistent and systematic point of view on this difficult and sometimes poorly understood problem. The authors emphasize the lack of universal and reliable criteria to compare different algorithms. They call for “richest descriptions for recognition processes” and “maximum use of the information present in the sensor data.” However, it is not clear at all what “richest,” “maximum us- e,” and “information” mean in this context. Despite a given mathematical definition of the problem the authors do not even try to apply the mathematical technique to quantitatively evaluate the presented algorithms. Perhaps an attempt should be made to develop such a technique in the spirit of recent advances in the theory of optimal algorithms [1]. There are a few minor shortcomings. The mathematical notation in the surface-representation schemes is inconsistent with the notation in the mathematical problem formulation. Several terms are undefined or ambiguous; however, this paper is a survey, and more detailed information can be obtained from the original literature. Overall, this paper is a good guide in the active field, and I would recommend it to professional researchers.
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