Filling the Occluded Area of Point Cloud with RGB-D Sensor
PubDate: Aug 2022
Teams: Southwest University；People’s Procuratorate of Beijing Municipality
Writers: Zhizhang Li; Junjian Huang; Zili Cao; Luting Zhang; Hailong Yan
Due to the occlusion of foreground objects in the camera’s perspective, some background areas have lack of depth and color data, which are reflected as blank areas on the point cloud model. Therefore, we propose a method to repair this part. In this paper, the REALSENSE D435 RGB-D (RGB-Depth) sensor is used to capture indoor environment, and the captured color and depth images that have been calibrated and denoised are generated corresponding point cloud models. Then perform OTSU threshold segmentation on the original depth image to segment the background part of the depth image that is occluded. The next step is to use traditional image inpainting criminisi algorithm to inpaint the depth and color image with the foreground removed. Therefore, the occluded part of the background of the point cloud model has been repaired. Finally, the original point cloud and the repaired point cloud are fused, and the result of the fusion is a relatively complete point cloud model.