发表机构
IRIT; CNRS; University of Zurich; The Mill; Fittingbox; TRACES; Balgrist University Hospital（IRIT; 法国国家科学研究中心; 苏黎世大学; The Mill; Fittingbox; TRACES; 巴尔格里斯特大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将多视角多光照表面重建的最新技术集成到开源摄影测量框架Meshroom中，为文化遗产领域提供可用的中间软件层，使先进重建方法更易用和评估。
AI 中文摘要
文化遗产文献记录越来越依赖于基于图像的3D表面重建，摄影测量软件使考古学家、保护人员和遗产技术人员能够使用这些工作流程。这些工具在常规多视角采集方面取得了成功，但它们并未常规利用更丰富的多视角、多光照数据，尽管这些数据在改善精细尺度表面重建方面具有潜力。这一限制在遗产背景下尤为重要，因为诸如RTI穹顶之类的受控光照采集设备已被用于捕获光照变化图像集。因此，挑战在于将这些现有的采集实践与最新的计算机视觉方法连接起来，并以可在实际遗产工作流程中使用的形式呈现。在这项工作中，我们通过将计算机视觉领域最先进的多视角、多光照表面重建组件集成到开源摄影测量框架Meshroom中来解决这一需求。我们的贡献并非提出新的重建算法，而是组装并展示现有的先进方法，即完整的光度立体视觉生态系统（标定、自标定和通用）、自动对象掩膜以及多视角法线与反射率集成，并将其置于一个面向遗产的可用的工作流程中。因此，所提出的系统在计算机视觉研究代码与实际文化遗产应用之间提供了一个中间软件层，使最新技术更易于使用和评估。
英文摘要
Cultural heritage documentation increasingly relies on image-based 3D surface reconstruction, with photogrammetry software making such workflows accessible to archaeologists, conservators, and heritage technicians. These tools have been successful for conventional multi-view acquisition, but they do not routinely exploit richer multi-view, multi-light data, despite its potential for improving fine-scale surface reconstruction. This limitation is particularly relevant in heritage contexts, where controlled-light acquisition devices such as RTI domes are already used to capture illumination-varying image sets. The challenge is therefore to connect these existing acquisition practices with recent computer vision methods in a form that can be used within operational heritage workflows. In this work, we address this need by integrating state-of-the-art components from computer vision for multi-view, multi-light surface reconstruction into Meshroom, an open-source photogrammetry framework. Rather than proposing a new reconstruction algorithm, our contribution is to assemble and expose existing advanced methods, namely a complete photometric stereo ecosystem (calibrated, self-calibrated and universal), automatic object masking, and multi-view normal-and-reflectance integration, within a usable heritage-oriented workflow. The proposed system thus provides an intermediate software layer between computer vision research code and practical cultural heritage applications, making recent techniques easier to use and evaluate.
Comments15 pages, 9 figures. Accepted at VISART VIII, ECCV 2026 workshops
