We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present a practical and efficient algorithm that uses this model to robustly recover shape from real images. Our method first reasons about shape locally in a dense set of patches in the observed image, producing shape distributions for every patch. These local distributions are then combined to produce a single consistent surface normal map. We demonstrate the efficacy of the approach through experiments on both synthetic renderings as well as real captured images.
An exception is the work of Fyffe al @cite_8 , who like us, rely on the statistics of natural albedos. They assume that surface albedo, as a function of spectral wavelength, is low-dimensional. Since this assumption doesn't provide an informative constraint for albedos in just three color channels, their setup involves multi-spectral capture under six spectrally distinct color sources. However, this requires a more complex imaging system and also suffers from lower light efficiency---since the visible spectrum now is split into six, instead of three, non-overlapping bands for both illumination and sensing. In contrast, we rely on the spatial, instead of spectral, statistics of albedos, and are able to employ regular three-channel RGB cameras.
@cite_8
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We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup—with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present a practical and efficient algorithm that uses this model to robustly recover shape from real images. Our method first reasons about shape locally in a dense set of patches in the observed image, producing shape distributions for every patch. These local distributions are then combined to produce a single consistent surface normal map. We demonstrate the efficacy of the approach through experiments on both synthetic renderings as well as real captured images. We present a method for acquiring the per-pixel diffuse albedo, specular albedo, and surface normal maps of a subject at a single instant in time. The method is single-shot, requiring no optical flow, and per-pixel, making no assumptions regarding albedo statistics or surface connectivity. We photograph the subject inside a spherical illumination device emitting a static lighting pattern of vertically polarized RGB color gradients aligned with the XYZ axes, and horizontally polarized RGB color gradients inversely aligned with the XYZ axes. We capture simultaneous photographs using one of two possible setups: a single-view setup using a coaxially aligned camera pair with a polarizing beam splitter, and a multi-view stereo setup with different orientations of linear polarizing filters placed on the cameras, enabling high- quality geometry reconstruction. From this lighting we derive full-color diffuse albedo, single-channel specular albedo suitable for dielectric materials, and polarization-preserving surface normals which are free of corruption from subsurface scattering. We provide simple formulae to estimate the diffuse albedo, specular albedo, and surface normal maps in the single-view and multi-view cases and show error bounds which are small for many common subjects including faces. We relax the long-held and problematic assumption in shape-from-shading (SFS) that albedo must be uniform or known, and address the problem of “shape and albedo from shading” (SAFS). Using models normally reserved for natural image statistics, we impose “naturalness” priors over the albedo and shape of a scene, which allows us to simultaneously recover the most likely albedo and shape that explain a single image. A simplification of our algorithm solves classic SFS, and our SAFS algorithm can solve the intrinsic image decomposition problem, as it solves a superset of that problem. We present results for SAFS, SFS, and intrinsic image decomposition on real lunar imagery from the Apollo missions, on our own pseudo-synthetic lunar dataset, and on a subset of the MIT Intrinsic Images dataset[15]. Our one unified technique appears to outperform the previous best individual algorithms for all three tasks. Our technique allows a coarse observation of shape (from a laser rangefinder or a stereo algorithm, etc) to be incorporated a priori. We demonstrate that even a small amount of low-frequency information dramatically improves performance, and motivate the usage of shading for high-frequency shape (and albedo) recovery. This paper describes a set of experimental measurements and theoretical calculations designed to recover both the surface-spectral reflectance function and the illuminant spectral-power distribution from the image data. A multichannel vision system comprising six color channels was created with the use of a monochrome CCD camera and color filters. The spectral sensitivity of each color channel is calibrated, and the dynamic range of the camera is extended for sensing a wide range of intensity levels. Three algorithms and the corresponding results are introduced. First, a method of choosing the appropriate dimension of the linear model dimensions is introduced. Second, the illuminant parameters are estimated from the sensor measurements made at multiple points within separate objects. Third, the sensor responses are corrected for highlight and shading variations. The body reflectance parameters, unique to each surface, are recovered from these corrected values. Experimental results with a small number of test surfaces and a simple illumination geometry demonstrate that the illuminant spectrum and the surface-spectral reflectance functions can be recovered to within typical deviations of 1 and 4 , respectively.