26 June 2017 A novel sparse-to-dense depth map generation framework for monocular videos
Author Affiliations +
Abstract
Generally, the process of monocular depth map generation consists of two stages: structure from motion and multi-view stereo. The multi-view stereo relies on the accuracy of the estimated camera pose and the photo-consistency assumption. However, the current methods cannot tackle the multi-view matching problem well because of the dependence on the accurate camera pose as well as the matching uncertainty. In this paper, to handle these issues, a new sparse-to-dense diffusion framework is put forward. First, the scene information is reconstructed from SFM (Structure From Motion) and the sparse point cloud is available instead of the camera pose. Secondly, the sparse depth point is re-projected to every frame as the depth label. Finally, the depth label is spread to the remaining pixels through a diffusion process. In addition, the edge detectors are used to make the propagation better-regulated. Experimentally, the results show that the proposed framework can robustly generate the depth map from monocular videos.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Runze Zhang, Runze Zhang, Zhiguo Cao, Zhiguo Cao, Qian Zhang, Qian Zhang, Yang Xiao, Yang Xiao, Ruibo Li, Ruibo Li, } "A novel sparse-to-dense depth map generation framework for monocular videos", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340H (26 June 2017); doi: 10.1117/12.2269885; https://doi.org/10.1117/12.2269885
PROCEEDINGS
8 PAGES


SHARE
RELATED CONTENT


Back to Top