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Some types of laser range scanner can measure range and color data simultaneously, and are often used to acquire 3D structure of outdoor scenery. However, unfortunately a laser range scanner cannot give us perfect range information about the target objects such as buildings, and various factors incur critical defects of range data. We present a defect detection scheme based on region segmentation using observed range-and-color data, and apply a nonlinear time-evolution method to the repair of defect regions of range data. As to the defect detection, performing range-and-color segmentation, we divide observed data into several regions corresponding to buildings, the sky, the ground, etc. Using the segmentation results, we determine defect regions as occlusion regions of buildings. Given defect regions, their range data will be repaired from the observed data in their neighborhoods. For that purpose, we adapt the time-evolution algorithm, originally developed for the repair of an intensity image, for the repair of range data.