• Corpus ID: 246063356

CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning

@article{Xu2022CPNetCR,
  title={CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning},
  author={Mingye Xu and Zhipeng Zhou and Hongbin Xu and Yali Wang and Yu Qiao},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.08215}
}
—Self-supervised learning has not been fully explored for point cloud analysis. Current frameworks are mainly based on point cloud reconstruction. Given only 3D coordinates, such approaches tend to learn local geometric structures and contours, while failing in understanding high level semantic content. Consequently, they achieve unsatisfactory performance in downstream tasks such as classification, segmentation, etc. To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network… 
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Masked Surfel Prediction for Self-Supervised Point Cloud Learning
TLDR
This work makes the first attempt, to the best of the knowledge, to consider the local geometry information explicitly into the masked auto-encoding, and proposes a novel Masked Surfel Prediction (MaskSurf) method, which outperforms its closest competitor, Point-MAE, by 1.2% on the real-world dataset of ScanObjectNN under the OBJ-BG setting.

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