Iterative closest points method based on photometric weight for 3D object reconstruction

Interests on 3D object reconstruction digitizing the shape and color of an object from the real world are getting popular. 3D object reconstruction consists of various steps such as image acquisition, image refinement, point cloud generation, iterative closest points, bundle adjustment and model surface representation. Among them, iterative closest points method is critical to calculate the accurate initial value for the optimization in the following bundle adjustment step. There is the object drift problem in the existing iterative closest points method due to the accumulated trajectory error as time flows. In this paper, we performed a more accurate registration between point clouds by SIFT features and the weighting on them. We found the proposed method decreases the absolute trajectory error and reduces the object drift problem in the reconstructed 3D object model.