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Light-field-depth-estimation network based on epipolar geometry and image segmentation.
Journal of the Optical Society of America A ( IF 1.9 ) Pub Date : 2020-06-30 , DOI: 10.1364/josaa.388555
Xucheng Wang , Chenning Tao , Rengmao Wu , Xiao Tao , Peng Sun , Yong Li , Zhenrong Zheng

In this paper, we propose a convolutional neural network based on epipolar geometry and image segmentation for light-field depth estimation. Epipolar geometry is utilized to estimate the initial disparity map. Multi-orientation epipolar images are selected as input data, and the convolutional blocks are adopted based on the disparity of different-direction epipolar images. Image segmentation is used to obtain the edge information of the central sub-aperture image. By concatenating the output of the two parts, an accurate depth map could be generated with fast speed. Our method achieves a high rank on most quality assessment metrics in the HCI 4D Light Field Benchmark and also shows effectiveness in estimating accurate depth on real-world light-field images.

中文翻译:

基于对极几何和图像分割的光场深度估计网络。

在本文中,我们提出了基于对极几何和图像分割的卷积神经网络,用于光场深度估计。利用对极几何来估计初始视差图。选择多向对极图像作为输入数据,并根据不同方向对极图像的视差采用卷积块。图像分割用于获得中央子孔径图像的边缘信息。通过将两个部分的输出串联起来,可以快速生成准确的深度图。在HCI 4D光场基准测试中,我们的方法在大多数质量评估指标上都获得了很高的评价,并且在估计真实世界光场图像上的准确深度方面也显示出有效性。
更新日期:2020-07-01
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