Real-time dense-view imaging for three-dimensional light-field display based on image color calibration and self-supervised view synthesis
Xiao Guo, Xinzhu Sang, Binbin Yan, Huachun Wang, Xiaoqian Ye, Shuo Chen, Huaming Wan, Ningchi Li, Zhehao Zeng, Duo Chen, Peng Wang, Shujun Xing
Abstract
Three-Dimensional (3D) light-field display has achieved promising improvement in recent years. However, since the dense-view images cannot be collected fast in real-world 3D scenes, the real-time 3D light-field display is still challenging to achieve in real scenes, especially at the high-resolution 3D display. Here, a real-time 3D light-field display method with dense-view is proposed based on image color correction and self-supervised optical flow estimation, and a high-quality and high frame rate of 3D light-field display can be realized simultaneously. A sparse camera array is firstly used to capture sparse-view images in the proposed method. To eliminate the color deviation of the sparse views, the imaging process of the camera is analyzed, and a practical multi-layer perception (MLP) network is proposed to perform color calibration. Given sparse views with consistent color, the optical flow can be estimated by a lightweight convolutional neural network (CNN) at high speed, which uses the input image pairs to learn the optical flow in a self-supervised manner. With inverse warp operation, dense-view images can be synthesized in the end. Quantitative and qualitative experiments are performed to evaluate the feasibility of the proposed method. Experimental results show that over 60 dense-view images at a resolution of 1024 × 512 can be generated with 11 input views at a frame rate over 20 fps, which is 4× faster than previous optical flow estimation methods PWC-Net and LiteFlowNet3. Finally, large viewing angles and high-quality 3D light-field display at 3840 × 2160 resolution can be achieved in real-time.