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Dense depth estimation frommultiple 360-degree images using virtual depth
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AI
DOI：10.1007/s10489-022-03391-w.png)
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摘要
En 中文
In this paper, we propose a dense depth estimation pipeline for multiview 360 degrees images. The proposed pipeline leverages a spherical camera model that compensates for radial distortion in 360 degrees images. The key contribution of this paper is the extension of a spherical camera model to multiview by introducing a translation scaling scheme. Moreover, we propose an effective dense depth estimation method by setting virtual depth and minimizing photonic reprojection error. We validate the performance of the proposed pipeline using the images of natural scenes as well as the synthesized dataset for quantitive evaluation. The experimental results verify that the proposed pipeline improves estimation accuracy compared to the current state-of-art dense depth estimation methods.
Keyword:
Depth estimation
Disparity estimation
360 degrees images
Multiview images
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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