High‐order Markov random field for single depth image super‐resolution

Abstract

Although there is an increasing interest in employing the depth data in computer vision applications, the spatial resolution of depth maps is still limited compared with typical visible-light images. A novel method is proposed to synthetically improve the spatial resolution of a single depth image. It integrates the higher-order terms into the Markov random field (MRF) formulation of example-based methods in order to improve the representational power of those methods. The inference is performed by approximately minimising the higher-order multi-label MRF energies. In addition, to improve the efficiency of the inference algorithm, a hierarchical scheme on the number of MRF states is proposed. First, a large number of states are used to obtain an initial labelling by solving the minimisation problem of inference for only the first-order energies. Then, the problem is solved for the higher-order energies in a smaller number of states. Performance comparisons show that proposed method improves the results of first-order approaches that are based on simple four-connected MRF graph structure, both qualitatively and quantitatively.

DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image

“…A lot of works have been proposed to improve the depth using an aligned highresolution color image. One family of approach is depth super-resolution that targets improving the resolution of the depth image [35,43,15,53,32,23,36,47]. These methods assume a low-resolution but dense depth map without missing signal.…”

Deep Depth Completion of a Single RGB-D Image

“…Several methods have been proposed to improve the spatial resolution of depth images using high-resolution color. They have exploited a variety of approaches, including Markov random fields [48,15,46,56,63], shape-from-shading [27,76], segmentation [45], and dictionary methods [21,34,49,69]. Although some of these techniques may be used for depth completion, the challenges of super-resolution are quite different -there the focus is on improving spatial resolution, where lowresolution measurements are assumed to be complete and regularly sampled.…”

Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

“…If we slide horizontally on the image, we can calculate this potential to detect sudden changes in depth [ 72 ]. This is computed by summation of these sudden depth horizontal windows ( ) in a square window ( ) and multiplying the figured-out value with corresponding disparity in the pixel, even the obstacles are not available.…”