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Range Image Registration Using a Photometric Metric under Unknown Lighting
Sept. 2013 (vol. 35 no. 9)
pp. 2252-2269
D. Thomas, Nat. Inst. of Inf., Tokyo, Japan
A. Sugimoto, Nat. Inst. of Inf., Tokyo, Japan
Based on the spherical harmonics representation of image formation, we derive a new photometric metric for evaluating the correctness of a given rigid transformation aligning two overlapping range images captured under unknown, distant, and general illumination. We estimate the surrounding illumination and albedo values of points of the two range images from the point correspondences induced by the input transformation. We then synthesize the color of both range images using albedo values transferred using the point correspondences to compute the photometric reprojection error. This way allows us to accurately register two range images by finding the transformation that minimizes the photometric reprojection error. We also propose a practical method using the proposed photometric metric to register pairs of range images devoid of salient geometric features, captured under unknown lighting. Our method uses a hypothesize-and-test strategy to search for the transformation that minimizes our photometric metric. Transformation candidates are efficiently generated by employing the spherical representation of each range image. Experimental results using both synthetic and real data demonstrate the usefulness of the proposed metric.
Index Terms:
lighting,feature extraction,image registration,image representation,image representation,range image registration,photometric metric,spherical harmonics representation,image formation,illumination,lighting,albedo value,point correspondence,photometric reprojection error,salient geometric feature,hypothesize-and-test strategy,Lighting,Image color analysis,Harmonic analysis,Cost function,Geometry,Photometry,photometric reprojection,Range image,registration,photometry,spherical harmonics
D. Thomas, A. Sugimoto, "Range Image Registration Using a Photometric Metric under Unknown Lighting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 9, pp. 2252-2269, Sept. 2013, doi:10.1109/TPAMI.2013.21
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