A learning-based method using epipolar geometry for light field depth estimation

Xucheng Wang; Wan Liu; Yan Sun; Lin Yang; Zhentao Qin; Zhenrong Zheng

doi:10.1117/12.2537208

18 November 2019 A learning-based method using epipolar geometry for light field depth estimation

Xucheng Wang, Wan Liu, Yan Sun, Lin Yang, Zhentao Qin, Zhenrong Zheng

Author Affiliations +

Proceedings Volume 11187, Optoelectronic Imaging and Multimedia Technology VI; 1118705 (2019) https://doi.org/10.1117/12.2537208
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

A novel method is proposed in this paper for light field depth estimation by using a convolutional neural network. Many approaches have been proposed to make light field depth estimation, while most of them have a contradiction between accuracy and runtime. In order to solve this problem, we proposed a method which can get more accurate light field depth estimation results with faster speed. First, the light field data is augmented by proposed method considering the light field geometry. Because of the large amount of the light field data, the number of images needs to be reduced appropriately to improve the operation speed, while maintaining the confidence of the estimation. Next, light field images are inputted into our network after data augmentation. The features of the images are extracted during the process, which could be used to calculate the disparity value. Finally, our network can generate an accurate depth map from the input light field image after training. Using this accurate depth map, the 3D structure in real world could be accurately reconstructed. Our method is verified by the HCI 4D Light Field Benchmark and real-world light field images captured with a Lytro light field camera.

Citation Download Citation

Xucheng Wang, Wan Liu, Yan Sun, Lin Yang, Zhentao Qin, and Zhenrong Zheng "A learning-based method using epipolar geometry for light field depth estimation", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 1118705 (18 November 2019); https://doi.org/10.1117/12.2537208

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