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NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer.

Xiaobo Hu1, Dejun Zhang1, Jinzhi Chen1

  • 1School of Computer Science, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

NrtNet is a novel unsupervised network for non-rigid point cloud registration, utilizing transformers to accurately map corresponding points in 3D shapes. This method achieves superior performance on complex registration tasks compared to existing approaches.

Keywords:
NrtNetnon-rigid point cloudregistrationself-attentivetransformerunsupervised

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Area of Science:

  • Computer Vision
  • Machine Learning
  • 3D Geometry Processing

Background:

  • Self-attention networks have advanced natural language processing and image analysis.
  • Point cloud registration is crucial for 3D data analysis.
  • Existing methods for non-rigid point cloud registration face challenges with large deformations.

Purpose of the Study:

  • To introduce NrtNet, the first transformer-based unsupervised network for non-rigid point cloud registration.
  • To address the challenge of large deformation non-rigid point cloud registration.
  • To improve the accuracy and efficiency of point cloud registration.

Main Methods:

  • NrtNet employs a transformer-based architecture for correspondence matrix generation.
  • The network extracts point-by-point features and learns correspondence probabilities.
  • A reconstruction module refines the registration by learning relative point drifts.

Main Results:

  • NrtNet demonstrates superior performance on synthetic and real-world non-rigid 3D shape datasets.
  • The proposed method outperforms state-of-the-art registration techniques.
  • NrtNet effectively handles large deformations in point cloud registration.

Conclusions:

  • NrtNet offers a powerful new approach for unsupervised non-rigid point cloud registration.
  • The transformer-based architecture is effective for learning point correspondences.
  • NrtNet advances the capabilities of 3D shape analysis and registration.