World's Best Scientists 2026 revealed!
Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
39
Citations
4107
World Ranking
704
National Ranking
110

Computer Science

D-Index
35
Citations
4244
World Ranking
11817
National Ranking
4825

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Zhizhong Han is affiliated with Wayne State University in the United States and has contributed extensively to several areas within computer science and engineering, particularly focusing on 3D shape modeling and analysis, computer graphics, and computer vision. Their research spans diverse topics such as advanced vision and imaging, image processing and 3D reconstruction, advanced numerical analysis techniques, and medical image segmentation techniques.

The scientist's work is distributed across prominent publication venues, including:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Image Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence

Zhizhong Han's research output includes a strong presence in computational mechanics, computer vision and pattern recognition, computer graphics and computer-aided design, geology, and artificial intelligence. These subfields indicate a multidisciplinary approach to modeling and reconstructing 3D data and advanced analytical techniques.

Notable recent papers authored by Zhizhong Han include:

  • SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction With Self-Projection Optimization, 2022, IEEE Transactions on Image Processing
  • Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Surface Reconstruction from Point Clouds by Learning Predictive Context Priors, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent coauthors collaborating with Zhizhong Han reflect a network of research relationships contributing to the advancement of 3D shape and image processing fields. These include:

  • Yu-Shen Liu
  • Matthias Zwicker
  • Xin Wen
  • Baorui Ma
  • Yan-Pei Cao

Best Publications

  • Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

    Xinhai Liu;Zhizhong Han;Yu-Shen Liu;Matthias Zwicker

  • SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

    Peng Xiang;Xin Wen;Yu-Shen Liu;Yan-Pei Cao

  • Point Cloud Completion by Skip-Attention Network With Hierarchical Folding

    Xin Wen;Tianyang Li;Zhizhong Han;Yu-Shen Liu

  • SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention

    Zhizhong Han;Mingyang Shang;Zhenbao Liu;Chi-Man Vong

  • SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization

    Yue Jiang;Dantong Ji;Zhizhong Han;Matthias Zwicker

  • PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths

    Xin Wen;Peng Xiang;Zhizhong Han;Yan-Pei Cao

  • 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation

    Zhizhong Han;Honglei Lu;Zhenbao Liu;Chi-Man Vong

  • Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds From Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction

    Zhizhong Han;Xiyang Wang;Yu-Shen Liu;Matthias Zwicker

  • View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions

    Zhizhong Han;Mingyang Shang;Yu-Shen Liu;Matthias Zwicker

  • Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

    Xin Wen;Zhizhong Han;Yan-Pei Cao;Pengfei Wan

  • Surface Reconstruction from Point Clouds by Learning Predictive Context Priors

    Unknown

  • Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

    Unknown

  • Snowflake Point Deconvolution for Point Cloud Completion and Generation With Skip-Transformer

    Unknown

  • L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

    Xinhai Liu;Zhizhong Han;Xin Wen;Yu-Shen Liu

  • Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds

    Unknown

  • SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization.

    Xinhai Liu;Xinchen Liu;Zhizhong Han;Yu-Shen Liu

  • 3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow

    Unknown

  • Y2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences

    Zhizhong Han;Mingyang Shang;Xiyang Wang;Yu-Shen Liu

  • Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network With Coupled Softmax

    Zhizhong Han;Zhenbao Liu;Chi-Man Vong;Yu-Shen Liu

  • Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine

    Zhizhong Han;Zhenbao Liu;Junwei Han;Chi-Man Vong

  • 3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

    Zhizhong Han;Zhizhong Han;Xiyang Wang;Chi Man Vong;Yu-Shen Liu

  • Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes

    Zhizhong Han;Zhenbao Liu;Junwei Han;Chi-Man Vong

  • Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion

    Tao Hu;Zhizhong Han;Abhinav Shrivastava;Matthias Zwicker

  • DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images

    Zhizhong Han;Chao Chen;Yu-Shen Liu;Matthias Zwicker

  • Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces

    Baorui Ma;Zhizhong Han;Yu-Shen Liu;Matthias Zwicker

Frequent Co-Authors

Matthias Zwicker
Matthias Zwicker University of Maryland, College Park
Chi-Man Vong
Chi-Man Vong University of Macau
Junwei Han
Junwei Han Northwestern Polytechnical University
C. L. Philip Chen
C. L. Philip Chen South China University of Technology
Yu-Kun Lai
Yu-Kun Lai Cardiff University
Abhinav Shrivastava
Abhinav Shrivastava University of Maryland, College Park
Xuelong Li
Xuelong Li China Telecom (China)
Yi Chang
Yi Chang Jilin University
Jianlong Fu
Jianlong Fu Microsoft (United States)
Ralph R. Martin
Ralph R. Martin Cardiff University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online options for advancing your computer science career can be both flexible and affordable. If you’re evaluating your next academic step, it’s important to consider which are the best degree to get based on your career goals in tech and the job market’s current needs.

For those seeking to upskill quickly, several short masters programs offer targeted learning—ideal if you want direct entry into specialized roles or management positions. If you are aiming for the highest academic level with financial efficiency in mind, explore the most affordable doctoral programs available through online platforms.

Educators or professionals in leadership may also opt for accelerated doctoral degrees, such as 2 year ed d programs online. These fast-track tracks are designed to get you advanced qualifications quicker, helping you open new career pathways in academia, research, or administration.

Best Scientists Citing Zhizhong Han

Trending Scientists

Recently Published Articles