Biography

I am pursuing my Master degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include data analysis, data mining and mathematical modeling, specifically in applications of user portrait and urban computing.

Research and Interests

• Urban Computing
• User Portrait
• Graph Representation

Publications

• Xin Kong, Guangyao Zhai, Baoquan Zhong, and Yong Liu. PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 3467–3473, 2019.
  [BibTeX] [Abstract] [DOI] [arXiv] [PDF]

  In this paper, we propose PASS3D to achieve point-wise semantic segmentation for 3D point cloud. Our framework combines the efficiency of traditional geometric methods with robustness of deep learning methods, consisting of two stages: At stage -1, our accelerated cluster proposal algorithm will generate refined cluster proposals by segmenting point clouds without ground, capable of generating less redundant proposals with higher recall in an extremely short time; stage -2 we will amplify and further process these proposals by a neural network to estimate semantic label for each point and meanwhile propose a novel data augmentation method to enhance the network’s recognition capability for all categories especially for non-rigid objects. Evaluated on KITTI raw dataset, PASS3D stands out against the state-of-the-art on some results, making itself competent to 3D perception in autonomous driving system. Our source code will be open-sourced. A video demonstration is available at https://www.youtube.com/watch?v=cukEqDuP_Qw.

  @inproceedings{kong2019pass3dpa,
  title = {PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud},
  author = {Xin Kong and Guangyao Zhai and Baoquan Zhong and Yong Liu},
  year = 2019,
  booktitle = {2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {3467--3473},
  doi = {https://doi.org/10.1109/IROS40897.2019.8968296},
  abstract = {In this paper, we propose PASS3D to achieve point-wise semantic segmentation for 3D point cloud. Our framework combines the efficiency of traditional geometric methods with robustness of deep learning methods, consisting of two stages: At stage -1, our accelerated cluster proposal algorithm will generate refined cluster proposals by segmenting point clouds without ground, capable of generating less redundant proposals with higher recall in an extremely short time; stage -2 we will amplify and further process these proposals by a neural network to estimate semantic label for each point and meanwhile propose a novel data augmentation method to enhance the network’s recognition capability for all categories especially for non-rigid objects. Evaluated on KITTI raw dataset, PASS3D stands out against the state-of-the-art on some results, making itself competent to 3D perception in autonomous driving system. Our source code will be open-sourced. A video demonstration is available at https://www.youtube.com/watch?v=cukEqDuP_Qw.},
  arxiv = {http://arxiv.org/pdf/1909.01643}
  }