面向可扩展的器官级三维植物分割：弥合数据、算法与计算之间的鸿沟
Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap
AI总结：
本研究综述了三维植物分割面临的三大挑战，提出了开源框架Plant Segmentation Studio，并通过实验验证了稀疏卷积和Transformer方法及合成数据策略，弥合了算法与部署间的鸿沟。
摘要：
植物形态的精确表征为植物与环境的相互作用及遗传进化提供了宝贵的见解。提取这些信息的一项关键技术是三维分割，它从复杂的点云中描绘出单个植物器官。尽管通用三维计算机视觉领域取得了显著进展，但三维分割在植物表型分析中的应用仍受到三大挑战的限制：i) 大规模标注数据集的稀缺，ii) 将先进深度神经网络适配到植物点云的技术困难，以及iii) 缺乏针对植物科学定制的标准化基准和评估协议。本综述通过以下方式系统性地解决这些障碍：i) 在通用三维分割领域的背景下，概述现有的三维植物数据集；ii) 系统总结基于深度学习的点云语义和实例分割方法；iii) 介绍Plant Segmentation Studio (PSS)，一个用于可复现基准测试的开源框架；以及iv) 进行广泛的定量实验，以评估代表性网络和模拟到现实（sim-to-real）学习策略。我们的研究结果强调了稀疏卷积骨干网络和基于Transformer的实例分割的有效性，同时也强调了基于建模和基于增强的合成数据生成在模拟到现实学习中减少标注需求的互补作用。总的来说，这项研究弥合了算法进步与实际部署之间的鸿沟，为研究人员提供了即时可用的工具，并为在三维植物表型分析中开发数据高效且可泛化的深度学习解决方案提供了路线图。数据和代码可在 https://github.com/perrydoremi/PlantSegStudio 获取。
英文摘要：
The precise characterization of plant morphology provides valuable insights into plant environment interactions and genetic evolution. A key technology for extracting this information is 3D segmentation, which delineates individual plant organs from complex point clouds. Despite significant progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges: i) the scarcity of large-scale annotated datasets, ii) technical difficulties in adapting advanced deep neural networks to plant point clouds, and iii) the lack of standardized benchmarks and evaluation protocols tailored to plant science. This review systematically addresses these barriers by: i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning strategies. Our findings highlight the efficacy of sparse convolutional backbones and transformer-based instance segmentation, while also emphasizing the complementary role of modeling-based and augmentation-based synthetic data generation for sim-to-real learning in reducing annotation demands. In general, this study bridges the gap between algorithmic advances and practical deployment, providing immediate tools for researchers and a roadmap for developing data-efficient and generalizable deep learning solutions in 3D plant phenotyping. Data and code are available at https://github.com/perrydoremi/PlantSegStudio.
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。
