Shanlin Sun (孙 山林)

@inproceedings{sun2024lidarf,
  title = {LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes},
  author = {Sun, Shanlin and Zhuang, Bingbing and Jiang, Ziyu and Liu, Buyu and Xie, Xiaohui and Chandraker, Manmohan},
  booktitle = {Computer Vision and Pattern Recognition},
  month = jun,
  year = {2024},
  note = {How to encode Lidar geometry guidance into NeRF training? How to deal with noisy/sparse Lidar supervision? How to augment data from Lidar data?},
}

@article{sun2022medical,
  title = {Medical Image Registration via Neural Fields},
  author = {Sun, Shanlin and Han, Kun and You, Chenyu and Tang, Hao and Kong, Deying and Naushad, Junayed and Yan, Xiangyi and Ma, Haoyu and Khosravi, Pooya and Duncan, James S and Xie, Xiaohui},
  journal = {Journal of Medical Image Analysis},
  month = jul,
  year = {2024},
  note = {Introducing neural fields into 3D medical image registration. It is lightweight and fast, can be displacement-based or velocity-based.},
}

@article{sun2023hybrid,
  title = {Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction},
  author = {Sun, Shanlin and Le, Thanh-Tung and You, Chenyu and Tang, Hao and Han, Kun and Ma, Haoyu and Kong, Deying and Yan, Xiangyi and Xie, Xiaohui},
  journal = {British Machine Vision Conference},
  month = nov,
  year = {2024},
  note = {Bridging mesh-based (explicit) and occupancy (implicit) shape representation with oriented point clouds (followed by differentiable poisson surface reconstruction) to model complex shapes with fewer local distortions.},
}

@inproceedings{sun2022topology,
  title = {Topology-preserving shape reconstruction and registration via neural diffeomorphic flow},
  author = {Sun, Shanlin and Han, Kun and Kong, Deying and Tang, Hao and Yan, Xiangyi and Xie, Xiaohui},
  booktitle = {Computer Vision and Patern Recognition},
  month = jun,
  year = {2022},
  note = {Introcuding topology preserving to template-based deep implicit shape representation via Neural ODE.},
}

@article{chen2021deep,
  title = {A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy},
  author = {Chen, Xuming and Sun, Shanlin and Bai, Narisu and Han, Kun and Liu, Qianqian and Yao, Shengyu and Tang, Hao and Zhang, Chupeng and Lu, Zhipeng and Huang, Qian and others},
  journal = {Radiotherapy and Oncology},
  volume = {160},
  pages = {175--184},
  year = {2021},
  note = {Segmenting more than 50 organs from whole body CT images. It is the core algorithm of a FDA-approved product.},
}