MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling. - Yue Qian; Junhui Hou; Yiming Zeng 002; Qijian Zhang; Sam Kwong; Ying He 001 (CoRR, 2020)

CorrNet3D: Unsupervised End-to-End Learning of Dense Correspondence for 3D Point Clouds. - Yiming Zeng 002; Yue Qian; Zhiyu Zhu; Junhui Hou; Hui Yuan 001; Ying He 001 (CVPR, 2021)

WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation. - Yingzhi Tang; Yue Qian; Qijian Zhang; Yiming Zeng 002; Junhui Hou; Xuefei Zhe (CVPR, 2022)

IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment. - Yiming Zeng 002; Yue Qian; Qijian Zhang; Junhui Hou; Yixuan Yuan; Ying He 001 (CVPR, 2022)

CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence. - Siyu Ren; Yiming Zeng 002; Junhui Hou; Xiaodong Chen (IEEE Trans. Circuits Syst. Video Technol., 2023)

Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis. - Qijian Zhang; Junhui Hou; Yue Qian; Yiming Zeng 002; Juyong Zhang; Ying He 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Task-Oriented Compact Representation of 3D Point Clouds via A Matrix Optimization-Driven Network. - Yue Qian; Junhui Hou; Qijian Zhang; Yiming Zeng 002; Sam Kwong; Ying He 001 (IEEE Trans. Circuits Syst. Video Technol., 2023)

Dynamic 3D Point Cloud Sequences as 2D Videos. - Yiming Zeng 002; Junhui Hou; Qijian Zhang; Siyu Ren; Wenping Wang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)