Creating 3D digital models of heritage sites typically involves laser scanning and photogrammetry. Although laser scan-derived point clouds provide detailed geometry, occlusions and hidden areas often lead to gaps. Terrestrial and UAV photography can largely fill these gaps and also enhance definition and accuracy at edges and corners. Historical buildings with complex architectural or decorative details require a systematically planned combination of laser scanning with handheld and UAV photography. High-resolution photography not only enhances the geometry of 3D building models but also improves their texturing. The use of cameras, especially UAV cameras, requires robust viewpoint planning to ensure sufficient coverage of the documented structure whilst minimising viewpoints for efficient image acquisition and processing economy. Determining ideal viewpoints for detailed modelling is challenging. Existing planners, relying on coarse scene proxies, often miss fine structures, significantly restrict the search space of candidate viewpoints and surface targets due to high computational costs, and are sensitive to surface orientation errors, which limits their applicability in complex scenarios. To address these limitations, we propose a strategy for generating sparse viewpoints from point clouds for efficient and accurate UAV-based modelling. Unlike existing planners, our backward visibility approach enables exploration of the camera viewpoint space at low computational cost and does not require surface orientation (normal vector) estimation. We introduce an observability-based planning criterion, a direction diversity-driven reconstructability criterion, which assesses modelling quality by encouraging global diversity in viewing directions, and a coarse-to-fine adaptive viewpoint search approach that builds on these criteria. The approach was validated on a number of complex heritage scenes. It achieves efficient modelling with minimal viewpoints and accurately captures fine structures, like thin spires, that are problematic for other planners. For our test examples, we achieve at least 98% coverage, using significantly fewer viewpoints, and with a consistently high structural similarity across all models.

中文翻译：

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基于无人机的稀疏视点规划框架，用于文化遗产古迹的详细 3D 建模


创建遗址的 3D 数字模型通常涉及激光扫描和摄影测量。尽管激光扫描衍生的点云提供了详细的几何图形，但遮挡和隐藏区域通常会导致间隙。地面和无人机摄影可以在很大程度上填补这些空白，还可以提高边缘和角落的清晰度和准确性。具有复杂建筑或装饰细节的历史建筑需要系统地规划激光扫描与手持和无人机摄影相结合。高分辨率摄影不仅可以增强 3D 建筑模型的几何图形，还可以改善其纹理。使用相机，尤其是无人机相机，需要强大的视点规划，以确保对记录的结构的充分覆盖，同时最大限度地减少视点以实现高效的图像采集和处理经济性。确定详细建模的理想视点是具有挑战性的。现有的规划器依赖于粗略的场景代理，经常错过精细结构，由于计算成本高，严重限制了候选视点和表面目标的搜索空间，并且对表面方向误差敏感，这限制了它们在复杂场景中的适用性。为了解决这些限制，我们提出了一种从点云生成稀疏视点的策略，以实现高效和准确的基于 UAV 的建模。与现有的规划器不同，我们的后向可见性方法能够以较低的计算成本探索相机视点空间，并且不需要表面方向（法向量）估计。 我们引入了基于可观察性的规划标准、方向多样性驱动的可重构性标准，该标准通过鼓励观察方向的全球多样性来评估建模质量，以及基于这些标准构建的从粗到细的自适应视点搜索方法。该方法在许多复杂的遗产场景中得到了验证。它以最少的视点实现高效的建模，并准确捕捉其他规划人员难以理解的精细结构，如薄尖顶。对于我们的测试示例，我们实现了至少 98% 的覆盖率，使用的视点明显更少，并且在所有模型中始终保持较高的结构相似性。