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基于图卷积网络对应估计的全自动校准主动立体3D内镜系统

Fully Auto-calibrated Active-stereo-based 3D Endoscopic System using Correspondence Estimation with Graph Convolutional Network.

作者信息

Furukawa Ryo, Oka Shiro, Kotachi Takahiro, Okamoto Yuki, Tanaka Shinji, Sagawa Ryusuke, Kawasaki Hiroshi

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4357-4360. doi: 10.1109/EMBC44109.2020.9176417.

DOI:10.1109/EMBC44109.2020.9176417
PMID:33018960
Abstract

We have developed a series of 3D endoscopic systems where a micro-sized pattern projector is inserted through the instrument channel of the endoscope and shapes are reconstructed by a structured light technique using captured images of the endoscopic camera. One problem of the previous works is that the accuracy of shape reconstruction is low, because the projector cannot be fixed to the endoscope, and thus, the pose of the pattern projector w.r.t. the camera cannot be pre-calibrated. In this paper, we propose a method to auto-calibrate the pose of the projector without using any special devices nor manual process. Since the technique is one-shot, multiple shapes can be reconstructed from an image sequence and a large 3D scene can be recovered by merging them. Experiments are conducted using the real system.

摘要

我们开发了一系列3D内窥镜系统,其中一个微型图案投影仪通过内窥镜的器械通道插入,并使用内窥镜摄像头捕获的图像通过结构光技术重建形状。以往工作的一个问题是形状重建的精度较低,因为投影仪无法固定在内窥镜上,因此图案投影仪相对于摄像头的姿态无法预先校准。在本文中,我们提出了一种无需使用任何特殊设备或手动过程即可自动校准投影仪姿态的方法。由于该技术是一次性的,因此可以从图像序列中重建多个形状,并通过合并它们来恢复大型3D场景。使用真实系统进行了实验。

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