315 research outputs found
Deep Reflectance Maps
Undoing the image formation process and therefore decomposing appearance into
its intrinsic properties is a challenging task due to the under-constraint
nature of this inverse problem. While significant progress has been made on
inferring shape, materials and illumination from images only, progress in an
unconstrained setting is still limited. We propose a convolutional neural
architecture to estimate reflectance maps of specular materials in natural
lighting conditions. We achieve this in an end-to-end learning formulation that
directly predicts a reflectance map from the image itself. We show how to
improve estimates by facilitating additional supervision in an indirect scheme
that first predicts surface orientation and afterwards predicts the reflectance
map by a learning-based sparse data interpolation.
In order to analyze performance on this difficult task, we propose a new
challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg)
using both synthetic and real images. Furthermore, we show the application of
our method to a range of image-based editing tasks on real images.Comment: project page: http://homes.esat.kuleuven.be/~krematas/DRM
How Multi-Illuminant Scenes Affect Automatic Colour Balancing
Many illumination-estimation methods are based on the assumption that the imaged scene is lit by a single course of illumination; however, this assumption is often violated in practice. We investigate the effect this has on a suite of illumination-estimation methods by manually sorting the Gehler et al. ColorChecker set of 568 images into the 310 of them that are approximately single-illuminant and the 258 that are clearly multiple-illuminant and comparing the performance of the various methods on the two sets. The Grayworld, Spatio-Spectral-Statistics and Thin-Plate-Spline methods are relatively unaffected, but the other methods are all affected to varying degrees
Terrain analysis using radar shape-from-shading
This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure
TWO-ILLUMINANT ESTIMATION AND USER-PREFERRED CORRECTION FOR IMAGE COLOR CONSTANCY
Master'sMASTER OF SCIENC
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