Revisiting Light Field Rendering With Deep Anti-Aliasing Neural Network

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 9; pp. 5430 - 5444
• Main Authors: Wu, Gaochang, Liu, Yebin, Fang, Lu, Chai, Tianyou
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aliasing; Artificial neural networks; Deep learning; Domains; Estimation; Image reconstruction; Interpolation; Light field reconstruction; light field rendering; Machine learning; Neural networks; Optimization; Pipelines; Rendering; Rendering (computer graphics); Shearing; view extrapolation
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2021.3073739

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity challenge using depth estimation followed by view synthesis or eschew explicit depth information to enable non-Lambertian rendering, but rarely solve both challenges in a unified framework. In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with advanced deep learning techniques. First, we analytically show that the essential issue behind the large disparity and non-Lambertian challenges is the aliasing problem. Classic LF rendering approaches typically mitigate the aliasing with a reconstruction filter in the Fourier domain, which is, however, intractable to implement within a deep learning pipeline. Instead, we introduce an alternative framework to perform anti-aliasing reconstruction in the image domain and analytically show comparable efficacy on the aliasing issue. To explore the full potential, we then embed the anti-aliasing framework into a deep neural network through the design of an integrated architecture and trainable parameters. The network is trained through end-to-end optimization using a peculiar training set, including regular LFs and unstructured LFs. The proposed deep learning pipeline shows a substantial superiority in solving both the large disparity and the non-Lambertian challenges compared with other state-of-the-art approaches. In addition to the view interpolation for an LF, we also show that the proposed pipeline also benefits light field view extrapolation.