Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University after getting my B.S. degree in Automation from Shandong University in 2022. My major research interests include Neural Radiance Fields and 3D Reconstruction.

Research and Interests

• Computer vision
• Neural Radiance Fields（NeRF）

Publications

• Zizhang Li, Xiaoyang Lyu, Yuanyuan Ding, Mengmeng Wang, Yiyi Liao, and Yong Liu. RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction. In 19th IEEE/CVF International Conference on Computer Vision (ICCV), pages 17715-17725, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Recently, neural implicit surfaces have become popular for multi-view reconstruction. To facilitate practical applications like scene editing and manipulation, some works extend the framework with semantic masks input for the object-compositional reconstruction rather than the holistic perspective. Though achieving plausible disentanglement, the performance drops significantly when processing the indoor scenes where objects are usually partially observed. We propose RICO to address this by regularizing the unobservable regions for indoor compositional reconstruction. Our key idea is to first regularize the smoothness of the occluded background, which then in turn guides the foreground object reconstruction in unobservable regions based on the object-background relationship. Particularly, we regularize the geometry smoothness of occluded background patches. With the improved background surface, the signed distance function and the reversedly rendered depth of objects can be optimized to bound them within the background range. Extensive experiments show our method outperforms other methods on synthetic and real-world indoor scenes and prove the effectiveness of proposed regularizations. The code is available at https://github.com/kyleleey/RICO

  @inproceedings{li2023rico,
  title = {RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction},
  author = {Zizhang Li and Xiaoyang Lyu and Yuanyuan Ding and Mengmeng Wang and Yiyi Liao and Yong Liu},
  year = 2023,
  booktitle = {19th IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages = {17715-17725},
  doi = {10.1109/ICCV51070.2023.01628},
  abstract = {Recently, neural implicit surfaces have become popular for multi-view reconstruction. To facilitate practical applications like scene editing and manipulation, some works extend the framework with semantic masks input for the object-compositional reconstruction rather than the holistic perspective. Though achieving plausible disentanglement, the performance drops significantly when processing the indoor scenes where objects are usually partially observed. We propose RICO to address this by regularizing the unobservable regions for indoor compositional reconstruction. Our key idea is to first regularize the smoothness of the occluded background, which then in turn guides the foreground object reconstruction in unobservable regions based on the object-background relationship. Particularly, we regularize the geometry smoothness of occluded background patches. With the improved background surface, the signed distance function and the reversedly rendered depth of objects can be optimized to bound them within the background range. Extensive experiments show our method outperforms other methods on synthetic and real-world indoor scenes and prove the effectiveness of proposed regularizations. The code is available at https://github.com/kyleleey/RICO}
  }