Zhiqi Li

Zhiqi Li

  • PGR
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Biography

Zhiqi delved into the intricate world of scene flow, captivated by the challenge of enabling machines to understand the dynamic relationships between objects in a given environment. Zhiqi looks forward to using neural point representation to extract geometric features of point clouds and improving the performance of scene flow estimation models on both synthetic data and real data.

Research

Her research interests include learning-based scene flow estimation and geometry processing.

Journal Articles

  • Chen, H., Li, Z., Wei, M. and Wang, J., 2023. Geometric and Learning-Based Mesh Denoising: A Comprehensive Survey. ACM Transactions on Multimedia Computing, Communications and Applications, 20 (3).
  • Li, Z., Xiang, N., Chen, H., Zhang, J. and Yang, X., 2023. Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends. Computer Graphics Forum, 42 (6).
  • Liu, Y., Yan, X., Li, Z., Chen, Z., Wei, Z. and Wei, M., 2023. PointGame: Geometrically and Adaptively Masked Autoencoder on Point Clouds. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-12.
  • Zhu, D., Zhang, Y., Li, Z., Wang, W., Xie, H., Wei, M., Cheng, G. and Wang, F.L., 2021. Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces. IEEE Transactions on Instrumentation and Measurement, 70.
  • Li, Z., Zhang, Y., Feng, Y., Xie, X., Wang, Q., Wei, M. and Heng, P.A., 2020. NormalF-Net: Normal Filtering Neural Network for Feature-preserving Mesh Denoising. CAD Computer Aided Design, 127.
  • Li, Z., Mao, X., Wang, J., Wei, M. and Guo, Y., 2020. Feature-Preserving Triangular Mesh Surface Denoising: A Survey and Prospective. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 32 (1), 1-15.