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Article

Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

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College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
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Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
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College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
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Author to whom correspondence should be addressed.
Academic Editor: Antonio Pepe
Sensors 2021, 21(14), 4628; https://doi.org/10.3390/s21144628
Received: 19 May 2021 / Revised: 21 June 2021 / Accepted: 29 June 2021 / Published: 6 July 2021
(This article belongs to the Section Intelligent Sensors)
The three-dimensional reconstruction method using RGB-D camera has a good balance in hardware cost and point cloud quality. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a 3D reconstruction method using Azure Kinect to solve these inherent problems. Shoot color images, depth images and near-infrared images of the target from six perspectives by Azure Kinect sensor with black background. Multiply the binarization result of the 8-bit infrared image with the RGB-D image alignment result provided by Microsoft corporation, which can remove ghosting and most of the background noise. A neighborhood extreme filtering method is proposed to filter out the abrupt points in the depth image, by which the floating noise point and most of the outlier noise will be removed before generating the point cloud, and then using the pass-through filter eliminate rest of the outlier noise. An improved method based on the classic iterative closest point (ICP) algorithm is presented to merge multiple-views point clouds. By continuously reducing both the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the integral color point cloud. Many experiments on rapeseed plants show that the success rate of cloud registration is 92.5% and the point cloud accuracy obtained by this method is 0.789 mm, the time consuming of a integral scanning is 302 s, and with a good color restoration. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower when building a automatic scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of rapeseed and other crops phenotype. View Full-Text
Keywords: three-dimensional reconstruction; ICP; Azure Kinect; RGB-D image processing; point cloud filtering; rapeseed three-dimensional reconstruction; ICP; Azure Kinect; RGB-D image processing; point cloud filtering; rapeseed
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MDPI and ACS Style

Teng, X.; Zhou, G.; Wu, Y.; Huang, C.; Dong, W.; Xu, S. Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera. Sensors 2021, 21, 4628. https://doi.org/10.3390/s21144628

AMA Style

Teng X, Zhou G, Wu Y, Huang C, Dong W, Xu S. Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera. Sensors. 2021; 21(14):4628. https://doi.org/10.3390/s21144628

Chicago/Turabian Style

Teng, Xiaowen, Guangsheng Zhou, Yuxuan Wu, Chenglong Huang, Wanjing Dong, and Shengyong Xu. 2021. "Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera" Sensors 21, no. 14: 4628. https://doi.org/10.3390/s21144628

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