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Proceedings Paper • new

A novel sparse-to-dense depth map generation framework for monocular videos
Author(s): Runze Zhang; Zhiguo Cao; Qian Zhang; Yang Xiao; Ruibo Li
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Paper 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.

Paper Details

Date Published: 26 June 2017
PDF: 8 pages
Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340H (26 June 2017); doi: 10.1117/12.2269885
Show Author Affiliations
Runze Zhang, Huazhong Univ. of Science and Technology (China)
Zhiguo Cao, Huazhong Univ. of Science and Technology (China)
Qian Zhang, Hubei Univ. (China)
Yang Xiao, Huazhong Univ. of Science and Technology (China)
Ruibo Li, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10334:
Automated Visual Inspection and Machine Vision II
Jürgen Beyerer; Fernando Puente León, Editor(s)

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