• Corpus ID: 235358236

FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration

@article{Xu2021FINetDB,
  title={FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration},
  author={Hao Xu and Nianjin Ye and Shuaicheng Liu and Guanghui Liu and Bing Zeng},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.03479}
}
Data association is important in the point cloud registration. In this work, we propose to solve the partial-to-partial registration from a new perspective, by introducing multi-level feature interactions between the source and the reference clouds at the feature extraction stage, such that the registra- tion can be realized without the attentions or explicit mask estimation for the overlapping detection as adopted previ- ously. Specifically, we present FINet, a feature interaction-based… 

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References

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TLDR
This work uses deep networks to tackle non-convexity of the alignment and partial correspondence problem in partial-to-partial point cloud registration, and shows PRNet predicts keypoints and correspondences consistently across views and objects.
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