Photometric visibility matrix for the automatic selection of optimal viewpoints

Abstract

Automated visual quality inspection is a core topic of robotics and computer vision. In industrial applications, the CAD model of the object to be inspected is often known and can be used to generate appropriate sensor poses (viewpoints) from which to inspect the object's surface and assure the quality of its geometry. Current approaches in this field generate optimal viewpoints by evaluating the geometric coverage but the photometric appearance of the object is usually not considered. This lack of photometric information results in a loss of crucial cues to establish actual visibility, especially when the object to inspect presents specular highlights (e.g., polished metal parts) and a complex geometry. In this paper, we propose integrating photometric information into the viewpoint evaluation to consider the object's appearance. To achieve this, we embed a bidirectional reflectance distribution function (BRDF) within the evaluation of the viewpoint candidates. We benchmark different BRDFs with increasingly realistic rendering to prove the concept of our approach. Specifically, we consider the Blinn-Phong and Cook-Torrance reflectance models. Our simulation results demonstrate the suitability and importance of using a photometric approach that considers material properties and selects optimal viewpoints for specific materials.