发表机构
Sardar Vallabhbhai National Institute of Technology; Indian Institute of Technology Bombay（萨达尔·瓦拉巴伊国家理工学院; 印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对时序植物重建数据集缺失的问题，提出SynthCrop4D数据集与结合去噪和Adaptive Temporal PoinTr的两阶段流程，提升了作物点云补全质量，可用于表型性状提取。
AI 中文摘要
高通量表型分析依赖于作物全生长阶段的精准三维重建，但由于缺乏具有完整几何真值的数据集，时序补全方法的开发与评估受到限制。为应对这一挑战，我们引入SynthCrop4D，这是一个程序生成的合成时序演化植物点云数据集，可为重建方法的基准测试提供可控的噪声、遮挡和完整植物几何信息。基于该数据集，我们评估了一种结合空间去噪与时序点云补全的两阶段流程：首先，一个去噪模块去除激光扫描原始点云的结构伪影；随后，得到的数据由Adaptive Temporal PoinTr模型处理，该模型利用前一生长阶段（t-1）的信息重建当前生长阶段（t），从而恢复因自遮挡缺失的区域。我们在SynthCrop4D和真实世界的Pheno4D数据集（含番茄和玉米）上，分别在去噪与不去噪的设置下评估所提框架。结果表明，去噪可大幅提升重建质量，最优配置在SynthCrop4D上的Chamfer距离为0.0061（时序PoinTr + Mamba-DG），在Pheno4D上的F值为0.2080（普通PoinTr + Mamba-DG）。我们进一步验证了补全点云在表型性状提取中的应用，包括株高、冠幅和凸包体积，在合成数据上的凸包体积平均绝对误差（MAE）为0.021，在真实数据上为0.343。综上，SynthCrop4D与所提流程为时序作物重建和高通量作物表型分析提供了基准与方法。
英文摘要
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Comments25 pages of pdf for manuscript , total 12 files including bbl and tex
