Three-dimensional shape measurement of a transparent object using a rangefinding approach

This paper describes a non-contact optical measuring approach by which to measure the three-dimensional (3D) shape of a transparent object such as a glass panel or an acrylic plate. In conventional approaches to obtain the 3D shape of a transparent object, contact-type sensors have been widely used. However, the measurement accuracy of contact-type sensors is susceptible to the influence of various factors. In this paper, we propose a novel triangulation-based rangefinding approach that can be applied to the 3D shape of a transparent object or to an opaque object. The rangefinder is based on the fact that the light projected onto the surface of a transparent object is in part reflected by the surface, though the majority of the projected light is transmitted through the surface. From the experimental results, the proposed rangefinding approach has the advantage that it can easily measure the 3D-shape of an object if the object reflects or transmits light, depending on its location. As a result, we conclude that the proposed approach has great potential for a wide range of industrial applications.</p

Three-dimensional scanning of specular and diffuse metallic surfaces using an infrared technique

For the past two decades, the need for three-dimensional (3-D) scanning of industrial objects has increased significantly and many experimental techniques and commercial solutions have been proposed. However, difficulties remain for the acquisition of optically non-cooperative surfaces, such as transparent or specular surfaces. To address highly reflective metallic surfaces, we propose the extension of a technique that was originally dedicated to glass objects. In contrast to conventional active triangulation techniques that measure the reflection of visible radiation, we measure the thermal emission of a surface, which is locally heated by a laser source. Considering the thermophysical properties of metals, we present a simulation model of heat exchanges that are induced by the process, helping to demonstrate its feasibility on specular metallic surfaces and predicting the settings of the system. With our experimental device, we have validated the theoretical modeling and computed some 3-D point clouds from specular surfaces of various geometries. Furthermore, a comparison of our results with those of a conventional system on specular and diffuse parts will highlight that the accuracy of the measurement no longer depends on the roughness of the surface

Reflectance Hashing for Material Recognition

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials