## 1 Introduction
Alternatively, another potent strategy involves modeling the point cloud data to specific geometric structures, such as ellipsoids [34], tubular structures [23], cylinders [35], or rings [36]. These techniques find particular relevance for organs exhibiting significant differential morphological structures. Apart from these direct methodologies that capitalize on morphological differences, an efficacious approach involves the use of spectral clustering for aggregating points of the same organ by calculating inter-point similarities [37]; however, this approach often incurs considerable computational costs. The combination of 2D imagery and 3D point clouds has also demonstrated commendable results in some plant studies [38; 39]. Mature plants often display organ overlap due to dense canopies, prompting noteworthy attempts to adopt skeletonization algorithms to extract the \"skeleton\" of the plant to simplify its morphological structural complexity [40]. Despite the success of these methodologies in some studies, they frequently necessitate the pre-computation of complex handcrafted features and are typically relevant only to specific morphological structures or closely related plant species, thereby limiting their widespread applicability. In contrast to traditional processing methods, deep learning approaches enable a data-driven feature extraction from the input data. These techniques eliminate the constraints of prior plant knowledge and facilitate organ segmentation of crop objects at varying growth stages without the need for intricate parameter tuning. Over recent years, a multitude of point cloud processing solutions based on deep learning have been broadly applied within the plant phenotyping domain. According to the diverse data input methods, these can be categorized into projection-based methods, voxel-based methods, and point-based methods. Projection-based methods involve the projection of 3D point clouds onto 2D planes to form images, subsequently analyzing these 2D images utilizing Convolutional Neural Networks (CNNs), and eventually mapping the segmentation results back into the 3D point space [41]. Some strategies establish correlations between 2D images and 3D point clouds via projection to enhance understanding of point clouds [42; 43]. However, these methods can be impeded by projection angles and occlusions, potentially leading to a loss of crucial structural information. An alternative approach involves mapping points into a 3D voxel grid. The voxelized data are then processed by 3D Convolutional Neural Networks (CNNs). For example, [PERSON] et al. presented a voxel-based Convolutional Neural Network (VCNN), specifically designed for the semantic segmentation of maize stems and leaves across various growth stages [44]. To address potential information loss during the mapping process, some researchers have proposed the concept of dynamic voxels [45], applying it to plant point cloud segmentation [46]. However, even with this approach, some point cloud information may be lost, and the size of the voxel significantly impacts network performance. Larger voxels often lose more detailed information, while smaller voxels usually lead to considerable computational costs. Point-based methods, on the other hand, extract features directly from individual points for segmentation. Outstanding advancements in point cloud data processing have been marked by frameworks such as PointNet [47] and PointNet++ [48], which both enable end-to-end learning. These frameworks can learn features directly from raw point cloud data without manual intervention. The algorithms PointNet and PointNet++ have seen substantial application in the field of plant phenotyping [49; 50; 51; 52; 53], with a host of scholars refining them further to enhance their performance.