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For instance, a recent paper [1] Zhang ZH, et.al can also simultaneously capture both 3D andcolor texture information using readily available hardware [1]. They claim four images arerequired for a RGB four phase shifted method, or a single image for a FFT method. Theirsolution to the color bleeding problem however requires a model of the color bleeding andusing nonlinear regression to find the color modulation and phase information.We added in Section 1, on page 4 to cite Zhang ZH and his collaborators four papers related tocolor problem:Zhang et al proposed a technique to alleviate the color coupling problems of the system [11, 12,13]. For this technique, it utilized a 3x3 matrix to represent the color coupling effect of thesystem, which was a pretty nice approach to deal with the color coupling problem. However,calibrating the color coupling matrix could be a complicated issue since each pixel might haveslightly different responses if the lens chromatic aberration is considered. In 2008, theyproposed another approach to deal with color bleeding (or coupling) [14]. For this approach,four fringe images are required (even though they claimed that a single image using a FFTmethod might be feasible, which have not been proven yet). However, this technique requiresa model of the color bleeding that uses nonlinear regression to find the color modulation andphase information. All above mentioned papers by Zhang and his collaborators tried to solvethe problem of measuring color and geometry simultaneously by utilizing 3‐CCD color cameras.However, none of them is suitable for real‐time 3D shape measurement since more than 3fringe images are utilized to deal with color related issues. In general, all the techniques usingcolor fringe images suffer the problem: they may not measure the surface profile properly. Forexample, for a red object, the green and blue light will be absorbed, and the informationcarried on by these color fringes will be lost.Another of Zhang ZH,et.al's recent publications [2] avoids the color coupling problem byprojecting RGB channels separately. Their computations for computing phase and color appearto be a four phase shifted fringe version of what appears in this paper by Zhang S. They pick theRGB channel with the largest modulation to compute the phase from, and use the RGBchannels to compute the color texture. Twelve RGB frames are needed to acquire the data(compared to three RGB frames for Zhang S and Yau). Zhang ZH's paper also shows there isinterest in this problem and gives further support to why this paper by Zhang S and Yau shouldlikewise be published.Same as aboveCONSThe paper does not seem to emphasize or adequately explain the principles and advantages ofthe main technique used to overcome the color bleeding and object color problems. The overemphasison using a Bayer color filter distracts from key points of the paper, especiallyconsidering a 3‐CCD camera should achieve the same results. The key points to solve the colorproblems should be presented, then the addition of the Bayer filter discussed.