Biography

I am pursuing my master degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interest is Visual SLAM.

Research and Interests

• Visual SLAM

Publications

• Tianxin Huang, Qingyao Liu, Xiangrui Zhao, Jun Chen, and Yong Liu. Learnable Chamfer Distance for Point Cloud Reconstruction. Pattern Recognition Letters, 178:43-48, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules. However, the static matching rules may deviate from actual shape differences. Although some works propose dynamically -updated learnable structures to replace matching rules, they need more iterations to converge well. In this work, we propose a simple but effective reconstruction loss, named Learnable Chamfer Distance (LCD) by dynamically paying attention to matching distances with different weight distributions controlled with a group of learnable networks. By training with adversarial strategy, LCD learns to search defects in reconstructed results and overcomes the weaknesses of static matching rules, while the performances at low iterations can also be guaranteed by the basic matching algorithm. Experiments on multiple reconstruction networks confirm that LCD can help achieve better reconstruction performances and extract more representative representations with faster convergence and comparable training efficiency.

  @article{huang2024lcd,
  title = {Learnable Chamfer Distance for Point Cloud Reconstruction},
  author = {Tianxin Huang and Qingyao Liu and Xiangrui Zhao and Jun Chen and Yong Liu},
  year = 2024,
  journal = {Pattern Recognition Letters},
  volume = 178,
  pages = {43-48},
  doi = {10.1016/j.patrec.2023.12.015},
  abstract = {As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules. However, the static matching rules may deviate from actual shape differences. Although some works propose dynamically -updated learnable structures to replace matching rules, they need more iterations to converge well. In this work, we propose a simple but effective reconstruction loss, named Learnable Chamfer Distance (LCD) by dynamically paying attention to matching distances with different weight distributions controlled with a group of learnable networks. By training with adversarial strategy, LCD learns to search defects in reconstructed results and overcomes the weaknesses of static matching rules, while the performances at low iterations can also be guaranteed by the basic matching algorithm. Experiments on multiple reconstruction networks confirm that LCD can help achieve better reconstruction performances and extract more representative representations with faster convergence and comparable training efficiency.}
  }