4,536 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
Exploration of a Polarized Surface Bidirectional Reflectance Model Using the Ground-Based Multiangle Spectropolarimetric Imager
Accurate characterization of surface reflection is essential for retrieval of aerosols using downward-looking remote sensors. In this paper, observations from the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI) are used to evaluate a surface polarized bidirectional reflectance distribution function (PBRDF) model. GroundMSPI is an eight-band spectropolarimetric camera mounted on a rotating gimbal to acquire pushbroom imagery of outdoor landscapes. The camera uses a very accurate photoelastic-modulator-based polarimetric imaging technique to acquire Stokes vector measurements in three of the instrument's bands (470, 660, and 865 nm). A description of the instrument is presented, and observations of selected targets within a scene acquired on 6 January 2010 are analyzed. Data collected during the course of the day as the Sun moved across the sky provided a range of illumination geometries that facilitated evaluation of the surface model, which is comprised of a volumetric reflection term represented by the modified Rahman-Pinty-Verstraete function plus a specular reflection term generated by a randomly oriented array of Fresnel-reflecting microfacets. While the model is fairly successful in predicting the polarized reflection from two grass targets in the scene, it does a poorer job for two manmade targets (a parking lot and a truck roof), possibly due to their greater degree of geometric organization. Several empirical adjustments to the model are explored and lead to improved fits to the data. For all targets, the data support the notion of spectral invariance in the angular shape of the unpolarized and polarized surface reflection. As noted by others, this behavior provides valuable constraints on the aerosol retrieval problem, and highlights the importance of multiangle observations.NASAJPLCenter for Space Researc
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