We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available.
Normals of surfaces with hybrid reflection properties can be recovered from purely radiometric considerations @cite_19 @cite_36 @cite_0 . Not before correspondences are available between pixels and the scene points they portray, tools from geometrical optics can be leveraged: identifying a single point light source renders the computation of the light map trivial; then, under known camera motion, an initial point can be expanded into a surface curve by tracking the location where the highlight first appeared @cite_11 . The standard structure-from-motion pipeline can also be enriched as to explicitly take into account specular reflections of a discrete set of scene points @cite_4 . Zheng and Murata @cite_26 progress from isolated point sources towards a one-dimensional concentric illuminant. The accuracy of their approach however remains limited as long as some points on the light source remain indistinguishable. Circular reflection lines suffice nevertheless for special applications such as measuring the cornea in human eyes @cite_24 or surface interrogation @cite_28 @cite_34 . Savarese and Perona @cite_37 study reflections of a two-dimensional checkerboard pattern.
@cite_37 @cite_26 @cite_4 @cite_36 @cite_28 @cite_0 @cite_19 @cite_24 @cite_34 @cite_11
2168633204 1698682053 217731264 1995309915 2162440529 2015578497 2170554555 1999562192
We recover 3D models of objects with specular surfaces. An object is rotated and its continuous images are taken. Circular-shaped light sources that generate conic rays are used to illuminate the rotating object in such a way that highlighted stripes can be observed on most of the specular surfaces. Surface shapes can be computed from the motions of highlights in the continuous images; either specular motion stereo or single specular trace mode can be used. When the lights are properly set, each point on the object can be highlighted during the rotation. The shape for each rotation plane is measured independently using its corresponding epipolar plane image. A 3D shape model is subsequently reconstructed by combining shapes at different rotation planes. Computing a shape is simple and requires only the motion of highlight on each rotation plane. The novelty of this paper is the complete modeling of a general type of specular objects that has not been accomplished before. Looking around in our every day environment, many of the encountered objects are specular to some degree. Actively using this fact when reconstructing objects from image sequences is the scope of the shape from specularities problem. One reason why this problem is important is that standard structure from motion techniques fail when the object surfaces are specular. Here this problem is addressed by estimating surface shape using information from the specular reflections. A specular reflection gives constraints on the surface normal. The approach differs significantly from many earlier shapes from specularities methods since the normal data used is sparse. The main contribution is to give a solid foundation for shape from specularities problems. Estimation of surface shape using reflections is formulated as a variational problem and the surface is represented implicitly using a level set formulation. A functional incorporating all surface constraints is proposed and the corresponding level set motion PDE is explicitly derived. This motion is then proven to minimize the functional. As a part of this functional a variational approach to normal alignment is proposed and analyzed. Also novel methods for implicit surface interpolation to sparse point sets are presented together with a variational analysis. Experiments on both real and synthetic data support the proposed method. We consider the problem of capturing shape characteristics on specular (refractive and reflective) surfaces that are nearly flat. These surfaces are difficult to model using traditional methods based on reconstructing the surface positions and normals. These lower-order shape attributes provide little information to identify important surface characteristics related to distortions. In this paper, we present a framework for recovering the higher-order geometry attributes of specular surfaces. Our method models local reflections and refractions in terms of a special class of multiperspective cameras called the general linear cameras (GLCs). We then develop a new theory that correlates the higher-order differential geometry attributes with the local GLCs. Specifically, we show that Gaussian and mean curvature can be directly derived from the camera intrinsics of the local GLCs. We validate this theory on both synthetic and real-world specular surfaces. Our method places a known pattern in front of a reflective surface or beneath a refractive surface and captures a distorted image on the surface. We then compute the optimal GLC using a sparse set of correspondences and recover the curvatures from the GLC. Experiments demonstrate that our methods are robust and highly accurate. In many remote sensing and machine vision applications, the shape of a specular surface such as water, glass, or polished met al must be determined instantaneously and under natural lighting conditions. Most image analysis techniques, however, assume surface reflectance properties or lighting conditions that are incompatible with these situations. To retrieve the shape of smooth specular surfaces, a technique known as specular surface stereo was developed. The method analyzes multiple images of a surface and finds a surface shape that results in a set of synthetic images that match the observed ones. An image synthesis model is used to predict image irradiance values as a function of the shape and reflectance properties of the surface, camera geometry, and radiance distribution of the illumination. The specular surface stereo technique was tested by processing four numerical simulations-a water surface illuminated by a low- and high-contrast extended light source, and a mirrored surface illuminated by a low- and high-contrast extended light source. Under these controlled circumstances, the recovered surface shape showed good agreement with the known input. > The orientation of patches on the surface of an object can be determined from multiple images taken with different illumination, but from the same viewing position. The method, referred to as photometric stereo, can be implemented using table lookup based on numerical inversion of reflectance maps. Here we concentrate on objects with specularly reflecting surfaces, since these are of importance in industrial applications. Previous methods, intended for diffusely reflecting surfaces, employed point source illumination, which is quite unsuitable in this case. Instead, we use a distributed light source obtained by uneven illumination of a diffusely reflecting planar surface. Experimental results are shown to verify analytic expressions obtained for a method employing three light source distributions. We present a new shape-from-distortion framework for recovering specular (reflective refractive) surfaces. While most existing approaches rely on accurate correspondences between 2D pixels and 3D points, we focus on analyzing the curved images of 3D lines which we call curved line images or CLIs. Our approach models CLIs of local reflections or refractions using the recently proposed general linear cameras (GLCs). We first characterize all possible CLIs in a GLC. We show that a 3D line will appear as a conic in any GLC. For a fixed GLC, the conic type is invariant to the position and orientation of the line and is determined by the GLC parameters. Furthermore, CLIs under single reflection refraction can only be lines or hyperbolas. Based on our new theory, we develop efficient algorithms to use multiple CLIs to recover the GLC camera parameters. We then apply the curvature-GLC theory to derive the Gaussian and mean curvatures from the GLC intrinsics. This leads to a complete distortion-based reconstruction framework. Unlike conventional correspondence-based approaches that are sensitive to image distortions, our approach benefits from the CLI distortions. Finally, we demonstrate applying our framework for recovering curvature fields on both synthetic and real specular surfaces. Abstract This paper examines the information available from the motion of specularities (highlights) due to known movements by the viewer. In particular two new results are presented. First, it is shown that for local viewer movements the concave convex surface ambiguity can be resolved without knowledge of the light source position. Second, the authors investigate what further geometrical information is obtained under extended viewer movements, from tracked motion of a specularity. The reflecting surface is shown to be constrained to coincide with a certain curve. However, there is some ambiguity — the curve is a member of a one-parameter family. Fixing one point uniquely determines the curve.