Sparse View Synthesis

Sparse View Synthesis is a computational method in image-based rendering that generates novel views of a scene from a limited set of input images by leveraging geometric and photometric priors to compensate for large inter-view disparities and occlusions.

45

Publications

17K

Citations

171

Authors

68

Institutions

Publications per year

2017–2026

37

Authors

171

  1. MT
    Matthew Tancik

    University of California, Berkeley, United States

    4 publications · 8.6K citations

    4

    h-index

  2. XZ
    Xiaowei Zhou

    Zhejiang University, China

    3 publications · 673 citations

    3

    h-index

  3. AK
    Angjoo Kanazawa

    University of California, Berkeley, United States

    3 publications · 2.7K citations

    3

    h-index

  4. RR
    Ravi Ramamoorthi

    University of California, Berkeley, United States

    3 publications · 6.6K citations

    3

    h-index

  5. SP
    Sida Peng

    Zhejiang University, China

    3 publications · 673 citations

    3

    h-index

1–5 of 171

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 245 citations

    9

    h-index

  2. ACM Transactions on Graphics

    TOG · 5 publications · 5.5K citations

    5

    h-index

  3. IEEE Transactions on Image Processing

    2 publications · 48 citations

    2

    h-index

  4. 1

    h-index

  5. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 1 publications · 19 citations

    1

    h-index

1–5