Ph.D. Candidate (2021)
School of Electronic Science and Engineering, Nanjing University
July. 2021 – present Ph.D. candidate in School of Electronic Science and Engineering, Nanjing University
Sep. 2018 – July. 2021
|M.S in School of Electronic Science and Engineering, Nanjing University|
|Sep. 2014 – July. 2018|
B.E. in School of Electronic Science and Engineering, Nanjing University
Point Cloud Compression (PCC) Sep. 2018 - present
Point cloud is a collection of points in 3D space, which can be used to represent 3D objects and scenes. Point cloud has huge potential applications in immersive media, autonomous driving, etc. It is a chanllenging problem to compress unstructured, and unordered 3D points for efficient storage and communication. Existing representative Point Cloud Compression (PCC) methods were developed by the MPEG, including G-PCC and V-PCC. We focus on more efficient and intelligent PCC technologies based on machine learning, named Learned PCC.
Jianqiang Wang, Hao Zhu, Haojie Liu and Zhan Ma, Lossy Point Cloud Geometry Compression via End-to-End Learning, in IEEE Trans. Circuits and Systems for Video Technology (TCSVT). [paper] [code] [video]
Linyao Gao, Tingyu Fan, Jianqiang Wang, Yilin Yu, Jun Sun, and Zhan Ma, Point Cloud Geometry Compression via Neural Graph Sampling, IEEE International Conference on Image Processing (ICIP), 2021.
Xuefei Yan, David J. Brady, Weiping Zhang, Changzhi Yu, Yulin Jiang, Jianqiang Wang, Chao Huang, Zian Li, and Zhan Ma, Compressive Sampling for Array Cameras, SIAM Imaging Sciences, 2021.