Xueqian Li

Higher Degree by Research Candidate

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology


https://lilac-lee.github.io/

Please visit my personal homepage at https://lilac-lee.github.io.

My Google Scholar page is at https://scholar.google.com/citations?user=aNnRnv0AAAAJ&hl=en.

  • Journals

    Year Citation
    2023 Zheng, J., Li, X., Ramasinghe, S., & Lucey, S. (2023). Robust Point Cloud Processing through Positional Embedding.
    2019 Sarode, V., Li, X., Goforth, H., Aoki, Y., Srivatsan, R. A., Lucey, S., & Choset, H. (2019). PCRNet: Point Cloud Registration Network using PointNet Encoding.
    2019 Sarode, V., Li, X., Goforth, H., Aoki, Y., Dhagat, A., Srivatsan, R. A., . . . Choset, H. (2019). One Framework to Register Them All: PointNet Encoding for Point Cloud
    Alignment.
  • Conference Papers

    Year Citation
    2024 Li, X., Zheng, J., Ferroni, F., Pontes, J. K., & Lucey, S. (2024). Fast Neural Scene Flow. In Proceedings of the IEEE International Conference on Computer Vision (pp. 9844-9856). Online: IEEE.
    DOI
    2022 Zheng, J., Ramasinghe, S., Li, X., & Lucey, S. (2022). Trading Positional Complexity vs Deepness in Coordinate Networks. In S. Avidan, G. Brostow, M. Cisse, G. M. Farinella, & T. Hassner (Eds.), Proceedings Computer Vision - ECCV Vol. 13687 LNCS (pp. 144-160). Tel Aviv, Israel: Springer Nature Switzerland.
    DOI Scopus6
    2022 Wang, C., Li, X., Pontes, J. K., & Lucey, S. (2022). Neural Prior for Trajectory Estimation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 6522-6532). Online: IEEE.
    DOI Scopus8
    2021 Li, X., Pontes, J. K., & Lucey, S. (2021). PointNetlk revisited. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 12758-12767). Nashville, TN, USA: IEEE COMPUTER SOC.
    DOI Scopus41 WoS7
    2021 Li, X., Pontes, J. K., & Lucey, S. (2021). Neural Scene Flow Prior. In Advances in Neural Information Processing Systems Vol. 34 (pp. 7838-7851). San Diego, CA, USA: Neural Information Processing Systems Foundation.
    Scopus23 WoS2
    2019 Yin, P., Xu, L., Li, X., Yin, C., Li, Y., Srivatsan, R. A., . . . He, Y. (2019). A multi-domain feature learning method for visual place recognition. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2019-May (pp. 319-324). IEEE.
    DOI Scopus19
    2019 Yin, P., Srivatsan, R. A., Chen, Y., Li, X., Zhang, H., Xu, L., . . . He, Y. (2019). MRS-VPR: A multi-resolution sampling based global visual place recognition method. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2019-May (pp. 7137-7142). IEEE.
    DOI Scopus10
  • Preprint

    Year Citation
    2024 Zheng, J., Li, X., & Lucey, S. (2024). Convolutional Initialization for Data-Efficient Vision Transformers.
    2024 Li, X., & Lucey, S. (2024). Fast Kernel Scene Flow.
    2024 Zheng, J., Li, X., & Lucey, S. (2024). Structured Initialization for Attention in Vision Transformers.
    2023 Li, X., Zheng, J., Ferroni, F., Pontes, J. K., & Lucey, S. (2023). Fast Neural Scene Flow.

TA (workshop) for COMP SCI 3315 -- Computer Vision (2023, 2024)

TA (workshop) for COMP SCI 4416/4816/7416 -- Applied Machine Learning (2023)


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