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Volume 20 Issue 3
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Wenpeng Xing, Jie Chen, Yike Guo. Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion. Machine Intelligence Research, vol. 20, no. 3, pp.408-420, 2023. https://doi.org/10.1007/s11633-022-1381-9
Citation: Wenpeng Xing, Jie Chen, Yike Guo. Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion. Machine Intelligence Research, vol. 20, no. 3, pp.408-420, 2023. https://doi.org/10.1007/s11633-022-1381-9
shu

Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion

doi: 10.1007/s11633-022-1381-9
  • 1.

    Department of Computer Science, Hong Kong Baptist University, Hong Kong 999077, China

  • 2.

    Data Science Institute, Imperial College London, London SW7 2AZ, UK

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  • Author Bio:

    Wenpeng Xing received the B. Eng. degree in civil engineering from Harbin Institute of Technology, China in 2017, the G. Dip. in computer engineering from University of Limerick, Ireland in 2019. He is currently a Ph. D. degree candidate in computer science at Department of Computer Science, Hong Kong Baptist University, China. His research interests include computational imaging, 3D content capture, modelling and rendering. E-mail: cswpxing@comp.hkbu.edu.hk ORCID iD: 0000-0001-5848-9417

    Jie Chen received the B. Sc. degree in Opto-Information science and engineering and the M. Eng. degree in optoelectronic information engineering both from School of Optical and Electronic Information, Huazhong University of Science and Technology, China in 2008 and 2011, respectly, and the Ph. D. degree in information engineering from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore in 2016. He is currently an assistant professor at Department of Computer Science, Hong Kong Baptist University, China. He worked as a post-doctoral research fellow at ST Engineering-NTU Corporate Laboratory, Singapore, and then as a senior algorithm engineer at OmniVision Technologies Inc. He currently serves as an Associate Editor for The Visual Computer, Springer. His research interests include computational photography (light fields, high dynamic range imaging, hyperspectral imaging and computational tomography), multimedia signal capture, reconstruction and content generation (3D vision, motion and music), AI for art-tech and humanities. E-mail: chenjie@comp.hkbu.edu.hk (Corresponding author) ORCID iD: 0000-0001-8419-4620

    Yike Guo received the B. Sc. degree (Hons.) in computing science from Tsinghua University, China in 1985, and the Ph. D. degree in computational logic from the Imperial College London, UK in 1994. He is currently a Chair professor in Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, where he also serves as the provost since December, 2022. He is professor of computing science in Department of Computing at Imperial College London (2002 – now, on leave since 2020 January). Between 2020 and 2022, he was a professor in Department of Computer Science, Hong Kong Baptist University, China, where he served as the vice-president (research and development). He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining, and large-scale data management. He founded InforSense, a software company for life science and health care data analysis, and served as the CEO for several years before the company′s merger with IDBS, a global advanced R&D software provider, in 2009. He is also the founding director of the Data Science Institute, Imperial College, UK, where he also led Discovery Science Group. He also holds the position of CTO of the tranSMART Foundation, a global open-source community using and developing data sharing and analytics technology for translational medicine. He has contributed to numerous major research projects, including the UK EPSRC platform project, Discovery Net; the Wellcome Trust-funded Biological Atlas of Insulin Resistance (BAIR); and the European Commission U-BIOPRED project. He is also the principal investigator of the European Innovative Medicines Initiative eTRIKS project, a 23 Million Euros project that is building a cloud-based informatics platform, in which tranSMART is a core component for clinicogenomic medical research, and the co-investigator of Digital City Exchange, a 5.9 Million GBP research program, exploring ways to digitally link utilities and services within smart cities. Since 2021, he is the principal coordinator of the 52.8 Million HKD project funded by Hong Kong Research Grants Council which investigates AI-based symbiotic creativity for Art-Tech. He has published over 250 articles, papers, and reports. Projects he has contributed to have been internationally recognized, including winning the “Most Innovative Data Intensive Application Award” at the Supercomputing 2002 conference for Discovery Net, the Bio-IT World “Best Practices Award” for U-BIOPRED in 2014 and the “Best Open Source Software Award” from ACM SIGMM in 2017. He is a Fellow of Royal Academy of Engineering (FREng), Member of Academia Europaea (MAE), Fellow of British Computer Society (FBCS), Fellow of Hong Kong Academy of Engineering Sciences (FHKEng). His research interests include AI for healthcare, data mining and art.E-mail: yikeguo@ust.hk; yikeguo@ust.hk ORCID iD: 0000-0002-3075-2161

  • Received Date: 2022-06-25
  • Accepted Date: 2022-10-18
  • Publish Online: 2023-03-17
  • Publish Date: 2023-06-01
    Abstract
  • Novel view synthesis has attracted tremendous research attention recently for its applications in virtual reality and immersive telepresence. Rendering a locally immersive light field (LF) based on arbitrary large baseline RGB references is a challenging problem that lacks efficient solutions with existing novel view synthesis techniques. In this work, we aim at truthfully rendering local immersive novel views/LF images based on large baseline LF captures and a single RGB image in the target view. To fully explore the precious information from source LF captures, we propose a novel occlusion-aware source sampler (OSS) module which efficiently transfers the pixels of source views to the target view′s frustum in an occlusion-aware manner. An attention-based deep visual fusion module is proposed to fuse the revealed occluded background content with a preliminary LF into a final refined LF. The proposed source sampling and fusion mechanism not only helps to provide information for occluded regions from varying observation angles, but also proves to be able to effectively enhance the visual rendering quality. Experimental results show that our proposed method is able to render high-quality LF images/novel views with sparse RGB references and outperforms state-of-the-art LF rendering and novel view synthesis methods.

     

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