基于多视角RGB图像序列的水稻植株三维建模数据

本水稻植株三维重建数据在作物表型测量与育种评价中具有重要应用价值。数据通过工业相机多视角采集与旋转平台匀速转动获取覆盖植株全方位的图像序列，并结合相机标定、稀疏重建与稠密重建生成稠密点云与网格模型，能够为水稻植株三维结构的高精度建模提供数据基础。在品种对比试验、不同生育期结构跟踪及栽培管理效果评估等场景中，该数据可支持对株高、冠层体积与形态、叶片空间分布、分蘖结构等关键结构性状进行量化分析与对比验证，减少人工测量误差，提高表型获取效率与一致性；同时可为三维重建算法在农业细长结构目标（叶片、茎秆）上的鲁棒性评测与工程化落地提供可复现的数据支撑，提升作物三维数字化分析与应用能力。

This 3D reconstruction dataset of rice plants holds significant application value in crop phenotyping measurement and breeding evaluation. The dataset is obtained by collecting omnidirectional image sequences of rice plants using multi-view industrial cameras and a uniformly rotating platform. Through camera calibration, sparse reconstruction and dense reconstruction, dense point clouds and mesh models are generated, which provides a foundational data basis for high-precision 3D structure modeling of rice plants. In scenarios including variety comparison trials, structural tracking at different growth stages, and cultivation management effect evaluation, this dataset enables quantitative analysis and comparative validation of key structural traits such as plant height, canopy volume and morphology, spatial distribution of leaves, and tiller structure. It reduces manual measurement errors, improves the efficiency and consistency of phenotyping data acquisition, and meanwhile offers reproducible data support for robustness evaluation and engineering deployment of 3D reconstruction algorithms targeting slender agricultural structures like leaves and stems, thereby enhancing the capability of 3D digital analysis and applications for crops.

搜集汇总

背景与挑战

背景概述

该数据集专注于利用多视角RGB图像序列进行水稻植株的三维建模，旨在通过视觉数据捕捉水稻植株的形态结构，为农业研究和精准管理提供三维可视化支持。它可能涉及图像采集、序列处理和三维重建技术，以生成高精度的植株模型，适用于生长监测、表型分析和自动化农业应用。