GCN-Calculated Graph-Feature Embedding for 3D Endoscopic System Based on Active Stereo

Michihiro Mikamo, Hiroshi Kawasaki, Ryusuke Sagawa, Ryo Furukawa

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

One of the promising fields for active-stereo sensors is medical applications such as 3D endoscope systems. For such systems, robust correspondence estimation between the detected patterns and the projected pattern is the most crucial. In this paper, we propose an auto-calibrating 3D endoscopic system using a 2D grid-graph pattern, where codes are embedded into each grid point. Since the pattern is a grid graph, we use a graph convolutional network (GCN) to calculate node-wise embedding accumulating code information of nearby grid points in the graph. The correspondence estimation using the GCN-calculated feature embedding is shown to be stable, even without using epipolar constraints. Using the correspondence estimation, we show that the auto-calibrating 3D measurement system can be realized. In the experiment, we confirmed that the proposed system achieved high accuracy and robust estimation comparing to the previous methods.

本文言語英語
ホスト出版物のタイトルFrontiers of Computer Vision - 27th International Workshop, IW-FCV 2021, Revised Selected Papers
編集者Hieyong Jeong, Kazuhiko Sumi
出版社Springer Science and Business Media Deutschland GmbH
ページ253-266
ページ数14
ISBN(印刷版)9783030816377
DOI
出版ステータス出版済み - 2021
イベント27th International Workshop on Frontiers of Computer Vision, IW-FCV 2021 - Virtual, Online
継続期間: 2 22 20212 23 2021

出版物シリーズ

名前Communications in Computer and Information Science
1405
ISSN(印刷版)1865-0929
ISSN(電子版)1865-0937

会議

会議27th International Workshop on Frontiers of Computer Vision, IW-FCV 2021
CityVirtual, Online
Period2/22/212/23/21

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 数学 (全般)

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