Chen Duo, Sang Xinzhu, Wang Peng, Yu Xunbo, Gao Xin, Yan Binbin, Wang Huachun, Qi Shuai, Ye Xiaoqian
Opt Express. 2021 Mar 1;29(5):7866-7884. doi: 10.1364/OE.419069.
Three-dimensional (3D) light-field display has achieved a great improvement. However, the collection of dense viewpoints in the real 3D scene is still a bottleneck. Virtual views can be generated by unsupervised networks, but the quality of different views is inconsistent because networks are separately trained on each posed view. Here, a virtual view synthesis method for the 3D light-field display based on scene tower blending is presented, which can synthesize high quality virtual views with correct occlusions by blending all tower results, and dense viewpoints on 3D light-field display can be provided with smooth motion parallax. Posed views are combinatorially input into diverse unsupervised CNNs to predict respective input-view towers, and towers of the same viewpoint are fused together. All posed-view towers are blended as a scene color tower and a scene selection tower, so that 3D scene distributions at different depth planes can be accurately estimated. Blended scene towers are soft-projected to synthesize virtual views with correct occlusions. A denoising network is used to improve the image quality of final synthetic views. Experimental results demonstrate the validity of the proposed method, which shows outstanding performances under various disparities. PSNR of the virtual views are about 30 dB and SSIM is above 0.91. We believe that our view synthesis method will be helpful for future applications of the 3D light-field display.
三维(3D)光场显示已经取得了很大的进步。然而,在真实3D场景中收集密集视点仍然是一个瓶颈。虚拟视图可以通过无监督网络生成,但由于网络是在每个姿态视图上单独训练的,不同视图的质量并不一致。在此,提出了一种基于场景塔融合的3D光场显示虚拟视图合成方法,该方法可以通过融合所有塔的结果来合成具有正确遮挡的高质量虚拟视图,并为3D光场显示上的密集视点提供平滑的运动视差。将姿态视图组合输入到不同的无监督卷积神经网络中,以预测各自的输入视图塔,并且将相同视点的塔融合在一起。所有姿态视图塔被融合为一个场景颜色塔和一个场景选择塔,从而可以准确估计不同深度平面处的3D场景分布。对融合后的场景塔进行软投影以合成具有正确遮挡的虚拟视图。使用去噪网络来提高最终合成视图的图像质量。实验结果证明了该方法的有效性,该方法在各种视差下均表现出色。虚拟视图的峰值信噪比约为30 dB,结构相似性指数高于0.91。我们相信我们的视图合成方法将有助于3D光场显示的未来应用。