Sai Bi

1.9k total citations · 1 hit paper
21 papers, 930 citations indexed

About

Sai Bi is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Computational Mechanics. According to data from OpenAlex, Sai Bi has authored 21 papers receiving a total of 930 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 13 papers in Computer Graphics and Computer-Aided Design and 9 papers in Computational Mechanics. Recurrent topics in Sai Bi's work include Advanced Vision and Imaging (15 papers), Computer Graphics and Visualization Techniques (13 papers) and 3D Shape Modeling and Analysis (9 papers). Sai Bi is often cited by papers focused on Advanced Vision and Imaging (15 papers), Computer Graphics and Visualization Techniques (13 papers) and 3D Shape Modeling and Analysis (9 papers). Sai Bi collaborates with scholars based in United States, Hong Kong and Israel. Sai Bi's co-authors include Kalyan Sunkavalli, Zexiang Xu, Yizhou Yu, Zhixin Shu, Xiaoguang Han, Qiangeng Xu, Julien Philip, Ulrich Neumann, Ravi Ramamoorthi and Hao Su and has published in prestigious journals such as ACM Transactions on Graphics, Computer Graphics Forum and IEEE Computer Graphics and Applications.

In The Last Decade

Sai Bi

21 papers receiving 903 citations

Hit Papers

Point-NeRF: Point-based Neural Radiance Fields 2022 2026 2023 2024 2022 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Point-NeRF: Point-based Neural Radiance Fields
    2022 322 citations Qiangeng Xu, Zexiang Xu et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sai Bi United States 12 792 443 318 109 104 21 930
Abhishek Kar United States 10 876 1.1× 442 1.0× 326 1.0× 130 1.2× 92 0.9× 12 988
Julien Philip United States 9 698 0.9× 526 1.2× 316 1.0× 83 0.8× 72 0.7× 14 835
Gabriel Schwartz United States 9 671 0.8× 407 0.9× 373 1.2× 81 0.7× 52 0.5× 12 819
Vickie Ye United States 5 755 1.0× 447 1.0× 335 1.1× 97 0.9× 71 0.7× 6 892
Jay Busch United States 18 927 1.2× 460 1.0× 242 0.8× 51 0.5× 52 0.5× 34 1.1k
Richard Tucker United States 10 1.0k 1.3× 558 1.3× 247 0.8× 93 0.9× 49 0.5× 16 1.1k
Tai-Pang Wu Hong Kong 15 616 0.8× 313 0.7× 165 0.5× 37 0.3× 91 0.9× 22 701
J. Oliensis United States 17 795 1.0× 223 0.5× 210 0.7× 260 2.4× 53 0.5× 44 943
Ryan Overbeck United States 11 774 1.0× 520 1.2× 185 0.6× 37 0.3× 27 0.3× 22 847

Countries citing papers authored by Sai Bi

Since Specialization
Citations

This map shows the geographic impact of Sai Bi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sai Bi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sai Bi more than expected).

Fields of papers citing papers by Sai Bi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sai Bi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sai Bi. The network helps show where Sai Bi may publish in the future.

Co-authorship network of co-authors of Sai Bi

This figure shows the co-authorship network connecting the top 25 collaborators of Sai Bi. A scholar is included among the top collaborators of Sai Bi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sai Bi. Sai Bi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Tan, Hao, Zhixin Shu, Yi Zhou, et al.. (2024). Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning. 6369–6379. 1 indexed citations
3.
Mei, Yiqun, et al.. (2024). Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image. 4263–4273. 3 indexed citations
4.
Bi, Sai, Zexiang Xu, Fujun Luan, et al.. (2024). Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling. 21157–21166. 2 indexed citations
5.
Xiao, Yan, Tiancheng Sun, Sai Bi, et al.. (2023). Neural Free‐Viewpoint Relighting for Glossy Indirect Illumination. Computer Graphics Forum. 42(4). 3 indexed citations
6.
Hašan, Miloš, et al.. (2023). MCNeRF: Monte Carlo Rendering and Denoising for Real-Time NeRFs. 1–11. 2 indexed citations
7.
Zhang, Xiaoshuai, Song-Fang Han, Sai Bi, et al.. (2023). TensoIR: Tensorial Inverse Rendering. 165–174. 49 indexed citations
8.
Kuang, Zhengfei, Fujun Luan, Sai Bi, et al.. (2023). PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields. 20691–20700. 26 indexed citations
9.
Xu, Qiangeng, Zexiang Xu, Julien Philip, et al.. (2022). Point-NeRF: Point-based Neural Radiance Fields. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5428–5438. 322 indexed citations breakdown →
10.
Gharbi, Michaël, Fujun Luan, Kalyan Sunkavalli, et al.. (2022). Differentiable Rendering of Neural SDFs through Reparameterization. 1–9. 30 indexed citations
11.
Zhang, Xiaoshuai, Sai Bi, Kalyan Sunkavalli, Hao Su, & Zexiang Xu. (2022). NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5439–5448. 66 indexed citations
12.
Bi, Sai, Stephen Lombardi, Shunsuke Saito, et al.. (2021). Deep relightable appearance models for animatable faces. ACM Transactions on Graphics. 40(4). 1–15. 1 indexed citations
13.
Li, Zhengqin, Sarah Wang, Meng Song, et al.. (2021). OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets. 7186–7195. 41 indexed citations
14.
Bi, Sai, Stephen Lombardi, Shunsuke Saito, et al.. (2021). Deep relightable appearance models for animatable faces. ACM Transactions on Graphics. 40(4). 1–15. 49 indexed citations
15.
Xu, Zexiang, Sai Bi, Kalyan Sunkavalli, et al.. (2019). Deep view synthesis from sparse photometric images. ACM Transactions on Graphics. 38(4). 1–13. 48 indexed citations
16.
Bi, Sai, Kalyan Sunkavalli, Federico Perazzi, et al.. (2019). Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement. 2730–2739. 20 indexed citations
17.
Bi, Sai, Nima Khademi Kalantari, & Ravi Ramamoorthi. (2018). Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition. Scientific Research and Essays. 53–63. 4 indexed citations
18.
Bi, Sai, Nima Khademi Kalantari, & Ravi Ramamoorthi. (2017). Patch-based optimization for image-based texture mapping. ACM Transactions on Graphics. 36(4). 1–11. 65 indexed citations
19.
Li, Guanbin, Sai Bi, Jue Wang, Yingqing Xu, & Yizhou Yu. (2016). ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos. IEEE Computer Graphics and Applications. 37(3). 70–81. 10 indexed citations
20.
Bi, Sai, Xiaoguang Han, & Yizhou Yu. (2015). AnL1image transform for edge-preserving smoothing and scene-level intrinsic decomposition. ACM Transactions on Graphics. 34(4). 1–12. 151 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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