通过彩色点云进行3D实例分割与重识别的园艺时间水果监测
Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds
AI总结：
本文提出利用彩色点云进行3D水果实例分割与重识别的方法，通过学习模型和注意力匹配网络实现动态果园中水果的准确监测。
摘要：
准确且一致的水果时间监测是实现自动化农业生产系统的关键步骤。然而，由于水果大小、形状、遮挡、方向及果园动态变化等因素，该任务具有挑战性。本文提出了一种新的方法，用于在时间序列中收集的3D地面点云上进行水果实例分割和重识别。我们的方法直接在密集的彩色点云上操作，捕捉细粒度的3D空间细节。我们使用基于学习的实例分割方法直接对点云进行分割。对于每个分割出的水果，我们提取一个紧凑且具有辨别力的描述符，使用3D稀疏卷积神经网络。为了在不同时间跟踪水果，我们引入了基于注意力的匹配网络，将水果与之前会话中的对应物关联起来。匹配通过概率分配方案进行，选择时间跨过的最可能关联。我们在实际世界的数据集上评估了我们的方法，证明其在实例分割和时间重识别方面优于现有方法，从而在复杂和动态的果园环境中实现了稳健和精确的水果监测。
英文摘要：
Accurate and consistent fruit monitoring over time is a key step toward automated agricultural production systems. However, this task is inherently difficult due to variations in fruit size, shape, occlusion, orientation, and the dynamic nature of orchards where fruits may appear or disappear between observations. In this article, we propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time. Our approach directly operates on dense colored point clouds, capturing fine-grained 3D spatial detail. We segment individual fruits using a learning-based instance segmentation method applied directly to the point cloud. For each segmented fruit, we extract a compact and discriminative descriptor using a 3D sparse convolutional neural network. To track fruits across different times, we introduce an attention-based matching network that associates fruits with their counterparts from previous sessions. Matching is performed using a probabilistic assignment scheme, selecting the most likely associations across time. We evaluate our approach on real-world datasets of strawberries and apples, demonstrating that it outperforms existing methods in both instance segmentation and temporal re-identification, enabling robust and precise fruit monitoring across complex and dynamic orchard environments. Keywords = Agricultural Robotics, 3D Fruit Tracking, Instance Segmentation, Deep Learning , Point Clouds, Sparse Convolutional Networks, Temporal Monitoring
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
