Reference grid-assisted network for 3D point signature learning from point clouds

Jing Zhu, Yi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Learning a robust 3D point signature from point clouds is an interesting but challenging task in the computer vision field due to the irregular and unordered structure characteristics of the point cloud data. In this paper, we propose to learn a 3D point signature by exploring the implicit relation between keypoints and their neighbors (grouped as patches) among the given scene point clouds. We design a uniform reference grid to represent the raw relation between each keypoint and its neighbors from the raw point clouds. In order to learn a 3D point signature gradually from expanding perceptive region, we create a novel siamese framework with a multi-layer perceptron (MLP)-based unit feature network and a 3D convolutional neural network (CNN)-based grid feature network. Specifically, the unit feature network aims to dig the connections among points fallen into the same unit of the reference grid, while the grid feature network is used to discover the grid-wise relations across the whole reference grid with concatenation of the learned unit- wise features. Moreover, we introduce an attention network upon the unit feature network to enhance the discriminative ability of our learned 3D point signature. Our proposed 3D point signature achieves superior performance over other state-of-the-art methods on keypoint matching and geometric registration on the real-world scenes datasets, e.g. SUN3D, 7-scenes and the synthetic scan augmented scenes in ICL-NUIM dataset. More importantly, our learned 3D point signature successfully handles the point cloud fragment alignment challenges by producing correct transformations with RANSAC algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages211-220
Number of pages10
ISBN (Electronic)9781728165530
DOIs
StatePublished - Mar 2020
Event2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States
Duration: Mar 1 2020Mar 5 2020

Publication series

NameProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020

Conference

Conference2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
CountryUnited States
CitySnowmass Village
Period3/1/203/5/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Zhu, J., & Fang, Y. (2020). Reference grid-assisted network for 3D point signature learning from point clouds. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 211-220). [9093270] (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV45572.2020.9093270