The exploitation of polarization information in the field of computer vision has become progressively more popular during the last few decades. This is primarily due to (1) the fact that polarization is a source of mostly untapped information for machine vision; (2) the relative computational ease by which geometrical information about a scene (e.g. surface normals) may be extracted from polarization data; and (3) the recent introduction of camera hardware able to capture polarization data in real time. The motivation for this paper is that a detailed quantitative study into the precision of polarization measurements with respect to expectation has yet to be performed. The paper therefore presents a detailed analysis and optimization of the key aspects of data capture necessary to acquire the most precise (as opposed to fast ) results for the benefit of future research into the field of “polarization vision.” The paper mainly focuses on a rotating polarizer method as this is shown to be the most accurate for high-sensitivity measurements. Commercial polarization cameras by contrast generally sacrifice precision for the benefit of much shorter capture times. That said, the paper reviews the state of the art in polarization camera technology and quantitatively evaluates the performance of one such camera: the Fraunhofer “POLKA.”

This paper presents a new database suitable for both 2-D and 3-D face recognition based on photometric stereo (PS): the Photoface database. The database was collected using a custom-made four-source PS device designed to enable data capture with minimal interaction necessary from the subjects. The device, which automatically detects the presence of a subject using ultrasound, was placed at the entrance to a busy workplace and captured 1839 sessions of face images with natural pose and expression. This meant that the acquired data is more realistic for everyday use than existing databases and is, therefore, an invaluable test bed for state-of-the-art recognition algorithms. The paper also presents experiments of various face recognition and verification algorithms using the albedo, surface normals, and recovered depth maps. Finally, we have conducted experiments in order to demonstrate how different methods in the pipeline of PS (i.e., normal field computation and depth map reconstruction) affect recognition and verification performance. These experiments help to 1) demonstrate the usefulness of PS, and our device in particular, for minimal-interaction face recognition, and 2) highlight the optimal reconstruction and recognition algorithms for use with natural-expression PS data. The database can be downloaded from http://www.uwe.ac.uk/research/Photoface.

We describe a robust method for the recovery of the depth map (or height map) from a gradient map (or normal map) of a scene, such as would be obtained by photometric stereo or interferometry. Our method allows for uncertain or missing samples, which are often present in experimentally measured gradient maps, and also for sharp discontinuities in the scene’s depth, e.g. along object silhouette edges. By using a multi-scale approach, our integration algorithm achieves linear time and memory costs. A key feature of our method is the allowance for a given weight map that flags unreliable or missing gradient samples. We also describe several integration methods from the literature that are commonly used for this task. Based on theoretical analysis and tests with various synthetic and measured gradient maps, we argue that our algorithm is as accurate as the best existing methods, handling incomplete data and discontinuities, and is more efficient in time and memory usage, especially for large gradient maps.

We describe a robust method to recover the depth coordinate from a normal or slope map of a scene, obtained e.g. through photometric stereo or interferometry. The key feature of our method is the fast solution of the Poisson-like integration equations by a multi-scale iterative technique. The method accepts a weight map that can be used to exclude regions where the slope information is missing or untrusted, and to allow the integration of height maps with linear discontinuities (such as along object silhouettes) which are not recorded in the slope maps. Except for pathological cases, the memory and time costs of our method are typically proportional to the number of pixels N. Tests show that our method is as accurate as the best weighted slope integrators, but substantially more efficient in time and space.

This paper presents a novel method for 3D surface reconstruction that uses polarization and shading information from two views. The method relies on polarization data acquired using a standard digital camera and a linear polarizer. Fresnel theory is used to process the raw images and to obtain initial estimates of surface normals, assuming that the reflection type is diffuse. Based on this idea, the paper presents two novel contributions to the problem of surface reconstruction. The first is a technique to enhance the surface normal estimates by incorporating shading information into the method. This is done using robust statistics to estimate how the measured pixel brightnesses depend on the surface orientation. This gives an estimate of the object material reflectance function, which is used to refine the estimates of the surface normals. The second contribution is to use the refined estimates to establish correspondence between two views of an object. To do this, a set of patches are extracted from each view and are aligned by minimizing an energy functional based on the surface normal estimates and local topographic properties. The optimum alignment parameters for different patch pairs are then used to establish stereo correspondence. This process results in an unambiguous field of surface normals, which can be integrated to recover the surface depth. Our technique is most suited to smooth, non-metallic surfaces. It complements existing stereo algorithms since it does not require salient surface features to obtain correspondences. An extensive set of experiments, yielding reconstructed objects and reflectance functions, are presented and compared to ground truth.

A new method for multiple viewpoint 3D shape reconstruction is presented that relies on the polarization properties of surface reflection. The method is intended to complement existing stereo techniques by establishing correspondence for surfaces without salient features. The phase and degree of polarization from two views of an object are used to reconstruct surface patches. Local surface properties are then used to both align these patches and to compute a cost for that alignment. This cost is used as a basis to establish correspondence between the two views. The method is tested on an object library comprising shapes of varying complexity and material. An accuracy assessment is also presented where real world data are compared to ground truth.

When unpolarized light is reflected from a smooth dielectric surface, it becomes partially polarized. This is due to the orientation of dipoles induced in the reflecting medium and applies to both specular and diffuse reflection. This paper is concerned with exploiting polarization by surface reflection, using images of smooth dielectric objects, to recover surface normals and, hence, height. This paper presents the underlying physics of polarization by reflection, starting with the Fresnel equations. These equations are used to interpret images taken with a linear polarizer and digital camera, revealing the shape of the objects. Experimental results are presented that illustrate that the technique is accurate near object limbs, as the theory predicts, with less precise, but still useful, results elsewhere. A detailed analysis of the accuracy of the technique for a variety of materials is presented. A method for estimating refractive indices using a laser and linear polarizer is also given.

A new technique for surface reconstruction is developed that uses polarization information from two views. One common problem arising from many multiple view techniques is that of finding correspondences between pixels on each image. In the new method, these correspondences are found by exploiting the spontaneous polarization of light caused by reflection to recover surface normals. These normals are then used to recover surface height. The similarity between reconstructed surface regions determines whether or not a pair of points correspond to each other. The technique is thus able to overcome the convex/concave ambiguity found in many single view techniques. Because the technique relies on smooth surface regions to detect correspondences, rather than feature detection, it is applicable to objects normally inaccessible to stereo vision. Also due to this fact, it is possible to remove noise without causing oversmoothing problems.

When unpolarised light is reflected from a smooth dielectric surface, it is spontaneously partially polarised. This process applies to both specular and diffuse reflection, although the effect is greatest for specular reflection. This paper is concerned with exploiting this phenomenon by processing images of smooth dielectric objects to recover surface normals and hence height. The paper presents the underlying physics of polarisation by reflection, starting with the Fresnel equations. It is explained how these equations can be used to obtain the shape of objects and some experimental results are presented to illustrate the usefulness of the theory. 1