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An Improved Discriminative Optimization for 3D Rigid Point Cloud Registration

arXiv 2104.08854 ↗First posted 18 Apr 2021cs.CVcited by 0

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Abstract (arXiv)

The Discriminative Optimization (DO) algorithm has been proved much successful in 3D point cloud registration. In the original DO, the feature (descriptor) of two point cloud was defined as a histogram, and the element of histogram indicates the weights of scene points in "front" or "back" side of a model point. In this paper, we extended the histogram which indicate the sides from "front-back" to "front-back", "up-down", and "clockwise-anticlockwise". In addition, we reweighted the extended histogram according to the model points' distribution. We evaluated the proposed Improved DO on the Stanford Bunny and Oxford SensatUrban dataset, and compared it with six classical State-Of-The-Art point cloud registration algorithms. The experimental result demonstrates our algorithm achieves comparable performance in point registration accuracy and root-mean-sqart-error.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 46 references that are in the map. First posted on arXiv 18 Apr 2021.

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References · 11

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  4. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 233
  5. 2020Deep Global Registrationcited by 229
  6. 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 142
  7. 2020Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondencescited by 109
  8. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  9. 2020Learning multiview 3D point cloud registrationcited by 78
  10. 2019DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registrationcited by 77
  11. 2015An Adaptive Data Representation for Robust Point-Set Registration and Mergingcited by 11

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