Maize is an important food crop both in the world, and its breeding work holds great significance. This paper conducts research in response to the requirements of three-dimensional (3D) seed testing for maize ears. Fifty maize ears with different morphologies were selected as samples. A 3D scanning device was used to obtain 3D point cloud data, and a textured 3D reconstruction model was obtained after processing. Preprocessing was carried out on the acquired point cloud data. Firstly, the Iterative Closest Point (ICP) algorithm was employed for point cloud registration to transform the point cloud data from different perspectives into the same coordinate system. Secondly, voxel filtering was used for point cloud denoising and smoothing to remove noise. A point cloud segmentation method based on the region-growing algorithm and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm were used to segment and cluster the maize kernels on the ears. The segmentation results of the maize kernels were obvious, and the Root Mean Square Error of the kernel count of the maize ear samples was 4.25, which was relatively close to the actual kernel count measured manually.