提出无需降采样的3D植物器官分割方法，适配多种物种和传感器。
OmniPlantSeg: Species Agnostic 3D Point Cloud Organ Segmentation for High-Resolution Plant Phenotyping Across Modalities
- 设计轻量级KD-SS算法，不依赖物种与传感器类型进行点云下采样。
- 可直接处理全分辨率点云，在多种模态下实现高精度分割。
- 适合需要跨物种、多传感器的高通量植物表型研究者使用。
准确的植物器官点云分割对3D植物表型分析至关重要。现有方法通常针对特定植物物种或特定传感器模态设计，且常需大量预处理并降采样点云以满足硬件或神经网络输入要求。本文提出一种简单而有效的点云子采样算法KD-SS，对传感器数据和植物物种均无依赖性。该方法无需降采样原始输入，从而支持对全分辨率点云进行分割。将KD-SS与当前主流分割模型结合，在多种模态（如摄影测量、激光三角测量、LiDAR）和多个植物物种上均取得良好效果。KD-SS可作为轻量级、保分辨率的替代方案，取代传统繁重的预处理与降采样流程，适用于任意物种与传感器组合的植物器官分割任务。
原文摘要 · Abstract (English)
Accurate point cloud segmentation for plant organs is crucial for 3D plant phenotyping. Existing solutions are designed problem-specific with a focus on certain plant species or specified sensor-modalities for data acquisition. Furthermore, it is common to use extensive pre-processing and down-sample the plant point clouds to meet hardware or neural network input size requirements. We propose a simple, yet effective algorithm KDSS for sub-sampling of biological point clouds that is agnostic to sensor data and plant species. The main benefit of this approach is that we do not need to down-sample our input data and thus, enable segmentation of the full-resolution point cloud. Combining KD-SS with current state-of-the-art segmentation models shows satisfying results evaluated on different modalities such as photogrammetry, laser triangulation and LiDAR for various plant species. We propose KD-SS as lightweight resolution-retaining alternative to intensive pre-processing and down-sampling methods for plant organ segmentation regardless of used species and sensor modality.
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