A Partitioned Joint Bilateral Filtering Method for 3D Reconstruction of Indoor Scenes
3D reconstruction based on RGB-D sensor is widely used in high-precision mapping.However,the im-age collected by depth camera has some problems,such as the loss of depth value and the influence of noise,which affect the accuracy of 3D point cloud mapping.To solve these problems,a partitioned joint bilateral filtering algorithm is proposed in this paper.The confidence of pixels is analyzed based on the Left Right Difference(LRD)of depth image and gray image,the missing pixels image are interpolated in depth image,and the partitioned joint bilateral filtering is realized based on confidence.Middbur standard database and Fast Sensor Motion Datase are used for qualitative analysis and is realized comparison to show that the joint bilateral filtering algorithm can smooth the noise effectively.Finally,the proposed algorithm is applied to 3D reconstruction.Experimental results show that the proposed algorithm can effectively repair the depth image and improve the accuracy of 3D point cloud map-ping and pose estimation.
filteringpartitioned joint bilateral filteringconfidence3D point cloud3D reconstruction
万方数据
维普
