Some types of laser range scanner measuring range and color data simultaneously are used to acquire 3D structure of outdoor scenery. However, a laser range scanner cannot give us perfect information about target objects such as buildings, and various factors incur defects of range data. We present a defect detection scheme based on the region segmentation using observed range and color data, and apply a time-evolution method to the repair of defective range data. As to the defect detection, performing the segmentation, we divide observed data into several regions corresponding to buildings, the ground and so on. Using the segmentation results, we determine defect regions. Given defect regions, their range data will be repaired from observed data in their neighborhoods. Reforming the transportation-based inpainting algorithm, previously developed for the defect repair of an intensity image, we construct a new defect-repair method that applies the interleaved sequential updates, composed of the transportation-based inpainting and the data projection onto the viewing direction of each range sample, to 3D point data converted from observed range data. The performance evaluations using artificially damaged test range data demonstrate that our repair method outperforms the existing repair methods both in quantitative performance and in subjective quality.