Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2021 IEEE/CVF International Conference on Computer Vision ICCV 2021
Subtitle of host publicationProceedings
PublisherIEEE
Pages2430-2439
ISBN (Electronic)978-1-6654-2812-5
ISBN (Print)978-1-6654-2813-2
Publication statusPublished - Oct 2021

Publication series

NameInternational Conference on Computer Vision (ICCV)
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

TitleIEEE International Conference on Computer Vision 2021
LocationVirtual
Period11 - 17 October 2021

Abstract

In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpolation, to replace the commonly-used geometry warping operation. Specifically, with the estimated geometric relation between input views, we first construct a lightweight neural network to dynamically learn weights for interpolating neighbouring pixels from input views to synthesize each pixel of novel views independently. In contrast to the fixed and content-independent weights employed in the geometry warping operation, the learned interpolation weights implicitly incorporate the correspondences between the source and novel views and adapt to different image content information. Then, we recover the spatial correlation between the independently synthesized pixels of each novel view by referring to that of input views using a geometry-based spatial refinement module. We also constrain the angular correlation between the novel views through a disparity-oriented LF structure loss. Experimental results on LF datasets with wide baselines show that the reconstructed LFs achieve much higher PSNR/SSIM and preserve the LF parallax structure better than state-of-the-art methods. The source code is publicly available at https://github.com/MantangGuo/DI4SLF.

Citation Format(s)

Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines. / Guo, Mantang; Jin, Jing; Liu, Hui; Hou, Junhui.

2021 IEEE/CVF International Conference on Computer Vision ICCV 2021: Proceedings. IEEE, 2021. p. 2430-2439 (International Conference on Computer Vision (ICCV)).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review