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DPDist: Comparing Point Clouds Using Deep Point Cloud Distance

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12356)

Abstract

We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally using the 3D modified Fisher vector representation. The local representation reduces the complexity of the surface, enabling effective learning, which generalizes well between object categories. We test the proposed distance in challenging tasks, such as similar object comparison and registration, and show that it provides significant improvements over commonly used distances such as Chamfer distance, Earth mover’s distance, and others.

Keywords

3D point clouds 3D computer vision 3D deep learning Distance Registration 

Notes

Acknowledgments

This research was supported by the Israel Science Foundation, and by the Israeli Ministry of Science and Technology.

Supplementary material

504452_1_En_32_MOESM1_ESM.pdf (561 kb)
Supplementary material 1 (pdf 561 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technion IITHaifaIsrael
  2. 2.Australian National University, Australian Centre for Robotic VisionCanberraAustralia

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