# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 1 Introduction  
With the rapid development of 3D sensing technology, point cloud data have become an important information source in fields such as autonomous driving, smart cities, crop phenotyping analysis, and intelligent manufacturing. In autonomous driving scenarios, precise point cloud segmentation helps improve vehicle localization and object detection accuracy, thereby enhancing the robustness of environmental perception [1]. In smart city construction, capturing the three-dimensional geometry of roads and buildings via point clouds can provide high-precision spatial data for urban planning and management [2]. In crop phenotyping analysis, point clouds can record the complete morphology of crops,providing a basis for leaf segmentation, organ recognition, and growth monitoring; in intelligent manufacturing and processing, point clouds are widely used for part modeling, online inspection, and reverse engineering to ensure product quality and production efficiency. However, point clouds are sparsely sampled and easily affected by noise; therefore, extracting stable, semantically rich mid-scale geometric features from a large number of discrete points remains a challenging research problem. In the field of 2D image segmentation, the concept of \"superpixels\" has been used to partition images into similar regions, thereby reducing computational complexity and improving the robustness of subsequent algorithms [3]. Traditional superpixel methods include watershed algorithms, clustering algorithms, energy function optimization, and graph-based approaches. The Watershed algorithm treats gray levels as \"terrain heights\" to extract watershed lines, while Waterpixels introduces a spatial regularity gradient on this basis to ensure more regular superpixel shapes [4; 5]. SLIC performs local k-means clustering in the color and spatial coordinate domains, achieving a balance between efficiency and quality [6]. SEEDS iteratively refines the superpixel boundaries so that the energy function gradually converges to an optimum [7]. Graph-based methods treat pixels as graph nodes, build a graph structure based on similarity, and then perform segmentation via normalized cuts [8]. With the rise of deep neural networks, superpixel segmentation has gradually evolved into an end-to-end trainable deep model. For example, the SSN introduces a differentiable soft clustering matrix to replace the rigid clustering of the traditional SLIC, achieving integrated training [9]; models such as SIN and AINet combine superpixel segmentation with downstream vision tasks, improving both segmentation accuracy and task alignment [10; 11]. These research outcomes demonstrate the advantages of mid-scale region segmentation in 2D vision tasks and provide valuable insights into 3D point cloud facet segmentation. In the 3D point cloud domain, facet over-segmentation and facet-based partitioning techniques have also attracted widespread attention. Traditional unsupervised clustering methods mostly rely on spatial connectivity and local geometric features, using threshold settings to obtain facets at different levels of granularity. For example, the 3D version of SLIC maps the point cloud into Euclidean space and then applies local k-means clustering; VCCS uses FPFH features to construct a voxel grid and employs breadth-first search to ensure that the segmented regions remain spatially connected, preventing labels from crossing semantic boundaries [6; 12]; PCA-based methods first project the point cloud onto local principal axes, control clustering granularity with distance and angle thresholds, and then use region growth to achieve instance segmentation [13]. Although these methods can generate reasonably complete facets to some extent, they heavily depend on threshold settings and hand-crafted features, making it difficult to simultaneously achieve robustness and computational efficiency across diverse scenes. In recent years, deep neural networks have made significant progress in 3D point-cloud segmentation.