Method for repair of defects in range data observed with a laser range scanner

Abstract

Some types of laser range scanner can measure both range data and color texture data simultaneously from the same viewpoint, and are often used to acquire 3D structure of outdoor scenery. However, for outdoor scenery, 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 method based on region segmentation using observed range and color data, and employ a nonlinear PDE (Partial Differential Equation)-based method to repair detected defect regions of range data. As to the defect detection, performing range-and-color segmentation, we divide observed data into several regions that correspond to buildings, trees, the sky, the ground, persons, street furniture, etc. Using the segmentation results, we extract occlusion regions of buildings as defects regions. Once the defect regions are extracted, 3D position data or range data will be repaired from the observed data in their neighborhoods. For that purpose, we adapt the digital inpainting algorithm, originally developed for the color image repair problem, for this 3D range data repair problem. This algorithm is formulated as the nonlinear time-evolution procedure based on the geometrical nonlinear PDE.