SCIENCE@home

SGPCR: Spherical Gaussian Point Cloud Representation and its Application to Object Registration and Retrieval

Driton Salihu,E. Steinbach

Published 2023 in IEEE Workshop/Winter Conference on Applications of Computer Vision

ABSTRACT

Retrieving and aligning CAD models from databases with scanned real-world point clouds remains an important topic for 3D reconstruction. Due to zero point-to-point correspondences between the sampled CAD model and the scanned real-world object, an information-rich representation of point clouds is needed. We propose SGPCR, a novel method for representing 3D point clouds by Spherical Gaussians for efficient, stable, and rotation-equivariant representation. We also propose a rotation-invariant convolution to improve the representation quality through a trainable optimization process. In addition, we demonstrate the strengths of SGPCR-based point cloud representation using the fundamental challenge of shape retrieval and point cloud registration on point clouds with zero point-to-point correspondences. Under these conditions, our approach improves registration quality by reducing chamfer distance by up to 90% and rotation root mean square error by up to 86% compared to the state of the art. Furthermore, the proposed SGCPR is used for one-shot shape retrieval and registration and improves retrieval precision by up to 58% over comparable methods.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    IEEE Workshop/Winter Conference on Applications of Computer Vision

  • Publication date

    2023-01-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/WACV56688.2023.00064
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-57 of 57 references · Page 1 of 1

CITED BY

Showing 1-9 of 9 citing papers · Page 1 of 1