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从博物馆标本的3D模型中标记的2D图像:结构从运动建模参数对染的2D图像的影响

Blair Mirka¹, Christopher Lippitt¹

¹Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, New Mexico, USA University of New Mexico Albuquerque United States of America.

|January 26, 2026

概括

焦点堆叠在机器学习应用中为3D模型准确性提供了最小的好处. 优化的光谱参数,不包括焦点堆叠,提高2D图像生成的几何和纹理精度.

科学领域:

数字遗产是数字遗产的一部分.
这是一种摄影计量技术 (photogrammetry).
机器学习机器学习

背景情况:

通过使用SfM (Structure from Motion) 创建博物馆和生物存储库的3D模型.
这些3D模型可以作为基于机器学习的物种识别的训练数据.
摄像度参数对2D染3D模型精度的影响尚未完全理解.

研究的目的:

调查光度参数如何影响使用SfM创建的3D模型的几何和纹理精度.
评估这些参数对来自3D模型的染2D图像质量的影响.
确定对机器学习应用程序的3D标本数字化工作流程中焦点堆叠的必要性.

主要方法:

使用SfM模拟了十个地面甲虫标本,形状,尺寸和颜色各不相同.
使用对形态特征的平均绝对误差 (MAE) 来定量评估几何准确性.
使用% RGB相似度和结构相似度指数 (SSIM) 测量了2D染纹理的精度.

主要成果:

与原始图像相比,对机器学习的目的提供了有限的焦点堆叠,没有任何好处.
几何精度和染的2D图像质量受到水平和垂直图像重叠的显著影响.
最优的方法涉及非焦点堆叠图像,具有特定的垂直和水平重叠参数.

结论:

焦点堆叠对于在机器学习中用于2D染的精确3D标本模型的创建并不重要.
优化图像重叠参数对于实现高几何和纹理精度至关重要.
放弃焦点堆叠可以是一种资源高效的方法,用于为深度学习算法生成准确的2D染.

关键词:

机器学习机器学习这就是SfMM.这是一种宏观摄影仪 (macro photogrammetry).综合数据综合数据

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