Integration of multiple-baseline color stereo vision with focus and defocus analysis for 3D shape measurement

A 3D vision system named SVIS is developed for three-dimensional shape measurement that integrates three methods: (i) multiple-baseline, multiple-resolution Stereo Image Analysis (SIA) that uses color image data, (ii) Image Defocus Analysis (IDA), and (iii) Image Focus Analysis (IFA). IDA and IFA are less accurate than stereo but they do not suffer from the correspondence problem associated with stereo. A rough 3D shape is first obtained using IDA and then IFA is used to obtain an improved estimate. The result is then used in SIA to solve the correspondence problem and obtain an accurate measurement of 3D shape. SIA is implemented using color (RGB) images recorded at multiple-baselines. Color images provide more information than monochrome images for stereo matching. Therefore matching errors are reduced and accuracy of 3D shape is improved. Further improvements are obtained through multiple-baseline stereo analysis. First short baseline images are analyzed to obtain an initial estimate of 3D shape. In this step, stereo matching errors are low and computation is fast since a shorter baseline results in lower disparities. The initial estimate of 3D shape is used to match longer baseline stereo images. This yields more accurate estimation of 3D shape. The stereo matching step is implemented using a multiple-resolution matching approach to reduce computation. First lower resolution images are matched and the results are used in matching higher resolution images. This paper presents the algorithms and the experimental results of 3D shape measurement on SVIS for several objects. These results suggest a practical vision system for 3D shape measurement.