Realtime Depth Map Noise Reduction for RGB-D Range Data Using Plane Fitting

RGB-D depth-map restoration using smooth depth neighborhood supports

A method to restore the depth map of an RGB-D image using smooth depth neighborhood (SDN) supports is presented. The SDN supports are computed based on the corresponding color image of the depth map. Compared with the most widely used square supports, the proposed SDN supports can well-capture the local structure of the object. Only pixels with similar depth values are allowed to be included in...

Shape Preserving RGB-D Depth Map Restoration

The RGB-D cameras have enjoined a great popularity these years. However, the quality of the depth maps obtained by such cameras is far from perfect. In this paper, we propose a framework for shape preserving depth map restoration for RGB-D cameras. The quality of the depth map is improved from three aspects: 1) the proposed region adaptive bilateral filter (RA-BF) smooths the depth noise across...

Comparison of Local Plane Fitting Methods for Range Data

In this research, we introduce a reasonable noise model for range data which is obtained by a laser radar range jnder, and derive two simple approximate solutions of the optimal local planejtting the range data under the noise model. Then we compare our methods with the general least-squares based methods, such as Z-function fitting, the eigenvalue method, and the maximum likelihood estimation ...

Depth-aware CNN for RGB-D Segmentation

Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high compu...

3-D Map Reconstruction from Range Data