PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

@article{Yu2021PoinTrDP,
  title={PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers},
  author={Xumin Yu and Yongming Rao and Ziyi Wang and Zuyan Liu and Jiwen Lu and Jie Zhou},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={12478-12487}
}
  • Xumin YuYongming Rao Jie Zhou
  • Published 19 August 2021
  • Computer Science
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Point clouds captured in real-world applications are of-ten incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud… 

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A new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloudpletion, which attains promising performance and can be extended to the scene-level point clouds completion scenario by designing a new geometry-enhanced semantic scene completion framework.

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