ROBUST CALIBRATION-MARKER AND LASER-LINE DETECTION FOR UNDERWATER 3D SHAPE RECONSTRUCTION BY DEEP NEURAL NETWORK

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

2 被引用数 (Scopus)

抄録

There are various demands for underwater 3D reconstruction, however, since most active stereo 3D reconstruction methods focus on the air environment, it is difficult to directly apply them to underwater due to the several critical reasons, such as refraction, water flow and severe attenuation. Typically, calibration-markers or laser-lines are strongly blurred and saturated by attenuation, which makes difficult to recover shape in the water. Another problem is that it is difficult to keep cameras, projectors and objects static in the water because of strong water flow, which prevents accurate calibration. In this paper, we propose a method to solve those problems by novel algorithm using deep neural network (DNN), epipolar constraint and specially designed devices. We also built a real system and tested it in the water, e.g., pool and sea. Experimental results confirmed the effectiveness of the proposed method. We also demonstrated real 3D scan in the sea.

本文言語英語
ホスト出版物のタイトル2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
出版社IEEE Computer Society
ページ4243-4247
ページ数5
ISBN(電子版)9781665496209
DOI
出版ステータス出版済み - 2022
イベント29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, フランス
継続期間: 10月 16 202210月 19 2022

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
ISSN(印刷版)1522-4880

会議

会議29th IEEE International Conference on Image Processing, ICIP 2022
国/地域フランス
CityBordeaux
Period10/16/2210/19/22

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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