Abstract
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.
Highlights
This paper proposes a three-dimensional reconstruction method using Azure Kinect and uses rapeseed as the object to test and verify the feasibility and performance of the method
The experiments were carried out on raw data obtained from 40 pots of rapeseed, in which there were 10 pots in each growth period
The morphological differences of rapeseed plants at this stage are not obvious, but they are significantly larger than the seedling stage, so the shooting distance has to be increased to ensure that a complete plant image is captured, which makes the difference between the point cloud quality and the seedling stage insignificant
Summary
Computer vision can be used for target recognition and positioning [1], growth status diagnosis [2], agricultural products classification [3], yield prediction [4], agricultural machinery automatic navigation [5], phenotypic [6] and many other agriculture field. Computer vision technology can analyze complete phenotypic parameters such as plant structure, shape, color and texture at one time, being capable of to quantitatively study the growth laws of crops [7]. Three-dimensional (3D) reconstruction is a major research topics of computer vision. It performs digital modeling of crops in the computer, and keeps of 3D geometry and color of plant shape and organs in the computer to achieve rapid, low-cost, and fast agronomic traits of crops. Accurate non-destructive measurement [8]
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