Liu Fei, Zhou Shubo, Wang Yunlong, Hou Guangqi, Sun Zhenan, Tan Tieniu
IEEE Trans Image Process. 2019 Sep 27. doi: 10.1109/TIP.2019.2943019.
Binocular stereo vision (SV) has been widely used to reconstruct the depth information, but it is quite vulnerable to scenes with strong occlusions. As an emerging computational photography technology, light-field (LF) imaging brings about a novel solution to passive depth perception by recording multiple angular views in a single exposure. In this paper, we explore binocular SV and LF imaging to form the binocular-LF imaging system. An imaging theory is derived by modeling the imaging process and analyzing disparity properties based on the geometrical optics theory. Then an accurate occlusion-robust depth estimation algorithm is proposed by exploiting multibaseline stereo matching cues and defocus cues. The occlusions caused by binocular SV and LF imaging are detected and handled to eliminate the matching ambiguities and outliers. Finally, we develop a binocular-LF database and capture realworld scenes by our binocular-LF system to test the accuracy and robustness. The experimental results demonstrate that the proposed algorithm definitely recovers high quality depth maps with smooth surfaces and precise geometric shapes, which tackles the drawbacks of binocular SV and LF imaging simultaneously.
双目立体视觉（SV）已被广泛用于重建深度信息，但它极易受到强遮挡场景的影响。作为一种新兴的计算摄影技术，光场（LF）成像通过在单次曝光中记录多个角度视图，为被动深度感知带来了一种新颖的解决方案。在本文中，我们探索双目SV和LF成像以形成双目-LF成像系统。通过对成像过程进行建模并基于几何光学理论分析视差特性，推导了一种成像理论。然后，利用多基线立体匹配线索和散焦线索，提出了一种精确的遮挡鲁棒深度估计算法。检测并处理由双目SV和LF成像引起的遮挡，以消除匹配模糊性和异常值。最后，我们开发了一个双目-LF数据库，并通过我们的双目-LF系统捕获真实场景，以测试准确性和鲁棒性。实验结果表明，所提出的算法能够明确地恢复具有平滑表面和精确几何形状的高质量深度图，同时解决了双目SV和LF成像的缺点。
