Geometry Guided Multi-Scale Depth Map Fusion via Graph Optimization

Abstract

In depth discontinuous and untextured regions, depth maps created by multiple view stereopsis are with heavy noises, but existing depth map fusion methods cannot handle it explicitly. To tackle the problem, two novel strategies are proposed: 1) a more discriminative fusion method, which is based on geometry consistency, measuring the consistency, and stability of surface geometry computed on both partial and global surfaces, different from traditional methods only using visibility consistency; 2) a graph optimization method which fuses pyramids of depth maps as mutual complementary information is available in different scales, and differs from existing multi-scale fusion methods. The method considers both sampling scale of a point and relations among points, and is proven to be solvable by graph cuts. Experimental results verify the superior performance of the proposed method to the traditional visibility consistency-based methods, and the proposed method is also compared favorably with a number of state-of-the-art methods. Moreover, the proposed method achieves the highest completeness among all the methods compared.

Temporally Consistent Depth Prediction With Flow-Guided Memory Units

“…D EPTH prediction from images plays a significant role in autonomous driving and advanced driver assistance systems, which helps understanding a geometric layout in a scene, and can be leveraged to solve other tasks, including vehicle/pedestrian detection [1], [2], traffic scene segmentation [3], and 3D reconstruction [4]. Stereo matching is a typical approach to recovering depth that finds dense correspondences between a pair of stereo images [5], [6], [7].…”