发表机构
The University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究对比评估了摄影测量法、NeRF等四种三维重建方法，发现基于NeRF的方法生成的实验室物品全息模型保真度最高，可为AR/MR教育环境提供沉浸式学习对象的实用流程。
AI 中文摘要
本研究旨在探究当前三维重建方法是否可支持构建用于教育用途的实验室对象的真实感全息表示。为此，我们对比了四种方法：摄影测量法、基于神经辐射场（NeRF）的方法、高斯溅射（Gaussian splatting）以及激光雷达（LiDAR）。这些方法被用于生成常见实验室物品的全息模型，由研究生对其保真度进行评估。参与者采用重复测量设计，从形状、颜色、纹理和视觉缺陷四个维度对模型进行评估。在所有实验对象中，基于NeRF的方法生成的表示始终具有最高的保真度，尤其适用于透明、反光或低纹理物品，这类物品是其他方法难以捕捉的。整体而言，形状和颜色的再现效果通常优于纹理，这表明部分视觉属性在教育全息图中仍难以准确呈现。除明确各重建方法的优势与局限外，本研究还展示了一套用于创建沉浸式学习对象的实用流程，该流程可支持基于增强现实（AR）/混合现实（MR）的教育环境中的实验前准备、空间推理及学生参与度提升。研究结果为开发沉浸式数字学习体验的教育工作者和研究人员提供了与设计相关的见解。
英文摘要
In this study, we examined whether current 3D reconstruction methods can support the creation of realistic holographic representations of laboratory objects for educational use. In this regard, we compared four approaches: photogrammetry, a neural radiance field (NeRF)-based method, Gaussian splatting, and LiDAR. These methods were used to generate holographic models of common laboratory items and their fidelity was evaluated by graduate students. Participants assessed the models for shape, color, texture, and visual defects using a repeated-measures design. Across objects, the NeRF-based method produced the most consistently high-fidelity representations, particularly for transparent, reflective, or low-texture items that were difficult to capture with other approaches. Shape and color were generally reproduced more successfully than texture, suggesting that some visual properties remain more challenging to represent accurately in educational holograms. Beyond identifying the strengths and limitations of each reconstruction method, the study demonstrates a practical workflow for creating immersive learning objects that may support pre-laboratory preparation, spatial reasoning, and student engagement in AR/MR-based educational environments. These findings offer design-relevant insights for educators and researchers developing immersive digital learning experiences.
Comments36 pages, 18 figures, 16 tables
