As technology advances and collaborations grow, our ability to finely quantify and explore morphological variation in 3D structures can enable important discoveries and insights into clinical, evolutionary, and genetic questions. However, it is critical to explore and understand the relative contribution of potential sources of error to the structures under study. In this study, we isolated the level of error in 3D facial images attributable to four sources, using the 3dMDface and Vectra H1 camera systems. When the two camera systems are used separately to image human participants, this analysis finds an upper bound of error potentially introduced by the use of the 3dMDface or Vectra H1 camera systems, in conjunction with the MeshMonk registration toolbox, at 0.44 mm and 0.40 mm, respectively. For studies using both camera systems, this upper bound increases to 0.85 mm, on average, and there are systematic differences in the representation of the eyelids, nostrils, and mouth by the two camera systems. Our results highlight the need for careful assessment of potential sources of error in 3D images, both in terms of magnitude and position, especially when dealing with very small measurements or performing many tests.

随着技术的进步和合作的发展，我们精细量化和探索 3D 结构形态变化的能力可以实现对临床、进化和遗传问题的重要发现和见解。然而，探索和理解潜在误差源对所研究结构的相对贡献至关重要。在本研究中，我们使用 3dMDface 和 Vectra H1 相机系统隔离了 3D 面部图像中可归因于四个来源的错误水平。当分别使用两个摄像头系统对人类参与者进行成像时，该分析发现，使用 3dMDface 或 Vectra H1 摄像头系统以及 MeshMonk 配准工具箱可能会引入误差上限，分别为 0.44 毫米和 0.40 毫米，分别。对于使用两种相机系统的研究，该上限平均增加到 0.85 毫米，并且两种相机系统对眼睑、鼻孔和嘴巴的表示存在系统差异。我们的结果强调需要仔细评估 3D 图像中潜在误差源（无论是幅度还是位置），尤其是在处理非常小的测量或执行许多测试时。