论文标题
主动估计遮挡和场景覆盖范围，以计划非结构化表示中的下一个最佳视图
Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation
论文作者
论文摘要
计划视图观察场景的过程被称为下一个最佳视图（NBV）问题。方法通常旨在获得高质量的场景观察，同时减少视图，旅行距离和计算成本的数量。 考虑阻塞和场景覆盖范围可以大大减少观察的视图和旅行距离的数量。结构化表示（例如，体素网格或表面网格）通常使用光线播放来评估代表结构的可见性，但这通常在计算上很昂贵。非结构化表示（例如，点密度）避免了维护和播放场景中施加的结构的计算开销，但因此并不能主动预测未来测量的成功。 本文介绍了用于处理闭塞的积极解决方案，并考虑了没有结构化表示的场景覆盖范围。通过扩展基于密度的表面边缘资源管理器（请参阅）来评估它们的性能。实验表明，这些技术允许非结构化表示能够观察到较少视图和距离较短的场景，同时保持高观察质量和低计算成本。
The process of planning views to observe a scene is known as the Next Best View (NBV) problem. Approaches often aim to obtain high-quality scene observations while reducing the number of views, travel distance and computational cost. Considering occlusions and scene coverage can significantly reduce the number of views and travel distance required to obtain an observation. Structured representations (e.g., a voxel grid or surface mesh) typically use raycasting to evaluate the visibility of represented structures but this is often computationally expensive. Unstructured representations (e.g., point density) avoid the computational overhead of maintaining and raycasting a structure imposed on the scene but as a result do not proactively predict the success of future measurements. This paper presents proactive solutions for handling occlusions and considering scene coverage with an unstructured representation. Their performance is evaluated by extending the density-based Surface Edge Explorer (SEE). Experiments show that these techniques allow an unstructured representation to observe scenes with fewer views and shorter distances while retaining high observation quality and low computational cost.
