About Me



I am a Lecturer (Assistant Professor) at Deakin University in Australia. I have spent more than two wonderful years as a Research Fellow in Singapore. Prior to that, I received my Ph.D in the College of Computer Science and Technology at Zhejiang University in June 2016. 

Research interests: my research interests mainly fall into the category of Visual Data Computing, for example, geometry modeling, processing and analysis, animation/simulation, 2D data processing and analysis. Please refer to DBLP for an up-to-date list of my research work.

Email: xuequan.lu AT deakin.edu.au

I am constantly looking for active Ph.D candidates, as well as visiting students/scholars. Please contact me (typically with a CV), if you are interested in visual data computing. 

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Recent News

10/2020 Our paper (Blur Removal via Blurred-Noisy Image Pair) accepted to IEEE TIP!
10/2020 Our paper (Federated Learning) accepted to IEEE TPDS!
09/2020 Our papers (Pointfilter (code) and Low-rank) accepted to IEEE TVCG!
09/2020 Papers accepted to Neural Networks and ICONIP 2020!
07/2020 Our code for Deep Feature-preserving Normal Estimation released!
06/2020 I serve as a PC member in ICONIP 2020 and papers accepted to CASA 2020 
04/2020 Our papers accepted to SPM 2020 (paper) and IJCNN 2020 (paper
12/2019 Our papers accepted to CVM 2020 (paper) and CAD journal (EXE released)
11/2019 Articulated skeleton results and EXE tools released (READ readme.txt)!
11/2019 I gave a talk at Southwest University (Joint Deakin-SWU Research Workshop)
09/2019 I serve as a PC member in CVM 2020
09/2019 papers accepted to CVIU, GECCO, ICONIP 2019 etc
07/2019 I serve as a PC member in ICONIP 2019
05/2019 new mesh denoising work released, with EXE program
05/2019 I gave a talk at Shanghai Jiao Tong University
02/2019 I joined School of Information Technology at Deakin University
12/2018 I gave a talk at Nanjing University of Aeronautics and Astronautics
11/2018 I gave a talk at SIAT of Chinese Academy of Sciences
07/2018 both 32bit and 64bit EXE released for GPF paper (GPF: GMM-inspired Feature-preserving Point Set Filtering)! Read enclosed PDF before using!
03/2018 new paper (Low Rank Matrix Approximation for Geometry Filtering) released!
01/2018 some test results released to compare with our CAGD17 and TVCG16
11/2017 our paper accepted to AAAI-2018!
07/2017 I gave a talk at Technical University of Munich, Germany
07/2017 I gave a talk at University of Applied Sciences – München, Germany
07/2017 I had an academic visit in Technical University of Munich, Germany
07/2017 our paper accepted to IEEE TVCG
02/2017 one paper accepted to CAGD