Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

Version 1 : Received: 19 May 2021 / Approved: 20 May 2021 / Online: 20 May 2021 (09:52:14 CEST)

How to cite: Teng, X.; Zhou, G.; Wu, Y.; Huang, C.; Dong, W.; Xu, S. 3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera. Preprints 2021, 2021050468 (doi: 10.20944/preprints202105.0468.v1). Teng, X.; Zhou, G.; Wu, Y.; Huang, C.; Dong, W.; Xu, S. 3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera. Preprints 2021, 2021050468 (doi: 10.20944/preprints202105.0468.v1).

Abstract

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. 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 three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing 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 complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. 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 and it is easy to automate the 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 crop phenotype.

Subject Areas

3D reconstruction; ICP; Azure Kinect; RGB-D image processing; point cloud filtering; rapeseed

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