Surface reconstruction for RGB-D data using real-time depth propagation

Real-time noise removal and depth propagation is a crucial component for surface reconstruction algorithms. Given the recent surge in the development of RGB-D sensors, a host of methods are available for detecting and tracking RGB-D features across multiple frames as well combining these frames to yield dense 3D point clouds. Nevertheless the sensor outputs are sparse in areas where textures are low (for traditional stereo cameras) and high reflectance regions (for Kinect like active sensors). It is crucial to employ a depth estimate propagation or diffusion algorithm to generate best approximation surface curvature in these regions for visualization. In this paper, we extend the Depth Diffusion using Iterative Back Substitution scheme to Kinect like RGB-D sensor data for real time surface reconstruction.