All-in-depth via Cross-baseline Light Field Camera

被引:0
作者
Jin, Dingjian [1 ]
Zhang, Anke [1 ]
Wu, Jiamin [1 ]
Wu, Gaochang [2 ]
Wang, Haoqian [1 ]
Fang, Lu [1 ]
机构
[1] Tsinghua Univ, Beijing, Peoples R China
[2] Northeastern Univ, Boston, MA 02115 USA
来源
MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA | 2020年
关键词
light field; depth map; EPI domain; cross-baseline; PATCHMATCH;
D O I
10.1145/3394171.3413974
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Light-field (LF) camera holds great promise for passive/general depth estimation benefited from high angular resolution, yet suffering small baseline for distanced region. While stereo solution with large baseline is superior to handle distant scenarios, the problem of limited angular resolution becomes bothering for near objects. Aiming for all-in-depth solution, we propose a cross-baseline LF camera using a commercial LF camera and a monocular camera, which naturally form a 'stereo camera' enabling compensated baseline for LF camera. The idea is simple yet non-trivial, due to the significant angular resolution gap and baseline gap between LF and stereo cameras. Fusing two depth maps from LF and stereo modules in spatial domain is fluky, which relies on the imprecisely predicted depth to distinguish close or distance range, and determine the weights for fusion. Alternatively, taking the unified representation for both LF and monocular sub-aperture view in epipolar plane image (EPI) domain, we show that for each pixel, the minimum variance along different shearing degrees in EPI domain estimates its depth with the highest fidelity. By minimizing the minimum variance, the depth error is minimized accordingly. The insight is that the calculated minimum variance in EPI domain owns higher fidelity than the predicted depth in spatial domain. Extensive experiments demonstrate the superiority of our cross-baseline LF camera in providing high-quality all-in-depth map from 0.2m to 100m.
引用
收藏
页码:3559 / 3567
页数:9
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