摘要
The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtain a dense disparity map by use of ground control points(GCPs) in the light field.Previous work optimizes the disparity map by local estimation which includes both reliable points and unreliable points. To reduce the negative effect of the unreliable points, we predict the disparity at non-GCPs from GCPs. Our method performs more robustly in shadow areas than previous methods based on GCP work, since we combine color information and local disparity. Experiments and comparisons on a public dataset demonstrate the effectiveness of our proposed method.
The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtain a dense disparity map by use of ground control points(GCPs) in the light field.Previous work optimizes the disparity map by local estimation which includes both reliable points and unreliable points. To reduce the negative effect of the unreliable points, we predict the disparity at non-GCPs from GCPs. Our method performs more robustly in shadow areas than previous methods based on GCP work, since we combine color information and local disparity. Experiments and comparisons on a public dataset demonstrate the effectiveness of our proposed method.
基金
supported by National Natural Science Foundation of China (Nos. 61272287, 61531014)
the State Key Laboratory of Virtual Reality Technology and Systems (No. BUAA-VR-15KF-10)
