3D Reconstruction and Volume Estimation of Jujube Using Consumer-Grade RGB-Depth Sensor

Abstract

In order to solve the problem of three-dimensional (3D) feature measurement in grading of jujubes, a volume and surface area measurement method based on 3D point cloud registration and reconstruction was proposed. First, use the RGB-D camera to collect the multi angle point cloud data of jujubes and perform filtering processing on it. Then, the Random Sample Consensus (RANSAC) algorithm was used to fit the plane and cylinder. After fitting, divide and remove the plane point cloud, obtain the central axis parameters of the cylinder, rotate the jujube around the central axis of the cylinder at a fixed angle, and collect the images of jujube from the same perspective, The Fast Point Feature Histogram (FPFH) and Sample Consciousness Initial Alignment (SAC-IA) algorithms were used to complete the 1/4 point cloud registration, the point to surface Iterative Closest Point (ICP) algorithm is used to complete the entire 1/2 registration, and the reconstructed jujube point cloud model is intercepted by the pass through algorithm. Finally, the jujubes point cloud model was smoothed and was filled of holes. The 3D coordinate method and convex hull method were used to calculate the jujube volume surface area. The correlation coefficients of the volume were 74.4 % and 74.5 %, and the correlation coefficients of the surface area was 83.2 % and 77.7 %. The experimental results show that this method can effectively calculate the 3D characteristic parameters of jujube, and can provide a reference for phenotype measurement and classification of jujube.