At present, optical sensors are being widely used to realize high quality control or reverse engineering of products, systems, buildings, environments or human bodies. Although the intrinsic characteristics of such breakthrough technologies may vary, ensuring complete acquisition relies on the definition of the optimal acquisition planning. To this end, the view planning problem (VPP) must be solved to automatically determine the optimal positions and/or trajectories of the acquisition devices to fully cover the part to be digitized. Such an automatization of the entire acquisition process is of considerably interest in the context of Industry 4.0. The aim of this paper is to review the state of the art works addressing the view planning problem and to identify the future challenges and possible research directions. First, the paper introduces a set of criteria to analyze the available methods, grouped into several macrocategories. The categories are presented and formalized to clearly understand the backbone and similarities of the grouped methods. Second, the paper describes and characterizes the available methods, based on their analysis according to the adopted criteria. The results of this extensive analysis clearly highlight the open issues and future challenges.

目前，光学传感器正被广泛用于实现产品、系统、建筑、环境或人体的高质量控制或逆向工程。尽管此类突破性技术的内在特征可能会有所不同，但确保完整的采购依赖于最佳采购计划的定义。为此，必须解决视图规划问题 (VPP) 以自动确定采集设备的最佳位置和/或轨迹，以完全覆盖要数字化的部分。在工业 4.0 的背景下，整个采集过程的这种自动化非常有趣。本文的目的是回顾解决视图规划问题的最新工作，并确定未来的挑战和可能的研究方向。第一的，该文件介绍了一组用于分析可用方法的标准，分为几个大类别。这些类别被呈现和形式化，以清楚地理解分组方法的主干和相似之处。其次，本文根据采用的标准进行分析，描述和描述了可用方法。这一广泛分析的结果清楚地突出了悬而未决的问题和未来的挑战。