The goal of visual surface inspection is to analyze an object’s surface and detect defects by looking at it from different angles. Developments over the past years have made it possible to partially automate this process. Inspection systems use robots to move cameras and obtain pictures that are evaluated by image processing algorithms. Setting up these systems or adapting them to new models is primarily done manually. A key challenge is to define camera viewpoints from which the images are taken. The number of viewpoints should be as low as possible while still guaranteeing an inspection of the desired quality. System engineers define and evaluate configurations that are improved based on a time-consuming trial-and-error process leading to a sufficient, but not necessarily optimal, configuration. With the availability of 3D surface models defined by triangular meshes, this step can be done virtually. This paper presents a new scalable approach to determine a small number of well-placed camera viewpoints for optical surface inspection planning. The initial model is approximated by B-spline surfaces. A set of geometric feature functionals is defined and used for an adaptive, non-uniform surface sampling that is sparse in geometrically low-complexity areas and dense in regions of higher complexity. The presented approach is applicable to solid objects with a given 3D surface model. It makes camera viewpoint generation independent of the resolution of the triangle mesh, and it improves previous results considering number of viewpoints and their relevance.

视觉表面检查的目的是分析对象的表面并通过从不同角度查看对象来检测缺陷。过去几年的发展使得该过程可以部分自动化。检查系统使用机器人移动相机并获取通过图像处理算法评估的图片。设置这些系统或使其适应新模型主要是手动完成的。一个关键的挑战是定义从中拍摄图像的相机视点。视点的数量应尽可能少，同时仍要保证检查所需的质量。系统工程师根据耗时的反复试验过程定义和评估经过改进的配置，从而获得足够但不一定是最佳的配置。利用三角形网格定义的3D表面模型，该步骤实际上可以完成。本文提出了一种新的可扩展方法，可以确定少量放置良好的相机视点以进行光学表面检查计划。初始模型由B样条曲面近似。定义了一组几何特征功能，并将其用于自适应的非均匀表面采样，该采样在几何复杂度低的区域稀疏，而在复杂度较高的区域则密集。所提出的方法适用于具有给定3D表面模型的实体对象。它使相机视点生成与三角形网格的分辨率无关，并且考虑到视点数量及其相关性，可以改善以前的结果。本文提出了一种新的可扩展方法，可以确定少量放置良好的相机视点以进行光学表面检查计划。初始模型由B样条曲面近似。定义了一组几何特征功能，并将其用于自适应的非均匀表面采样，该采样在几何复杂度低的区域稀疏，而在复杂度较高的区域则密集。所提出的方法适用于具有给定3D表面模型的实体对象。它使相机视点生成与三角形网格的分辨率无关，并且考虑到视点数量及其相关性，可以改善以前的结果。本文提出了一种新的可扩展方法，可以确定少量放置良好的相机视点以进行光学表面检查计划。初始模型由B样条曲面近似。定义了一组几何特征功能，并将其用于自适应的非均匀表面采样，该采样在几何复杂度低的区域稀疏，而在复杂度较高的区域则密集。所提出的方法适用于具有给定3D表面模型的实体对象。它使相机视点生成与三角形网格的分辨率无关，并且考虑到视点数量及其相关性，可以改善以前的结果。定义了一组几何特征功能，并将其用于自适应的非均匀表面采样，该采样在几何复杂度低的区域稀疏，而在复杂度较高的区域则密集。所提出的方法适用于具有给定3D表面模型的实体对象。它使相机视点生成与三角形网格的分辨率无关，并且考虑到视点数量及其相关性，可以改善以前的结果。定义了一组几何特征功能，并将其用于自适应的非均匀表面采样，该采样在几何复杂度低的区域稀疏，而在复杂度较高的区域则密集。所提出的方法适用于具有给定3D表面模型的实体对象。它使相机视点生成与三角形网格的分辨率无关，并且考虑到视点数量及其相关性，可以改善以前的结果。