{"content":[{"text":"Multiple Directional Illuminant Estimation from a Single Image","bbox":{"left":0.166673,"top":0.128732,"width":0.6392410000000001,"height":0.025086999999999998}},{"text":"Yang Wang, Dimitris Samaras \n Computer Science Department, \n State University of New York at Stony Brook, NY 11794-4400, USA \n {yangwang, samaras}@cs.sunysb.edu","bbox":{"left":0.216319,"top":0.179548,"width":0.539948,"height":0.08321900000000002}},{"text":"Abstract","bbox":{"left":0.238554,"top":0.2856,"width":0.072687,"height":0.02090599999999998}},{"text":"We present a new method for the detection and estima- \n tion of multiple directional illuminants, using only one sin- \n gle image of an object of arbitrary known geometry. The \n surface is not assumed to be pure Lambertian, instead, it \n can have both Lambertian and specular properties. We \n propose a novel methodology that integrates information \n from shadows, shading and specularities in the presence of \n strong directional sources of illumination, even when sig- \n niﬁcant non-directional sources exist in the scene. Since the \n specular spots have much sharper intensity changes than \n the Lambertian part, we can locate them in the image of the \n sphere by ﬁrst down-sampling the image and then applying \n a region growing algorithm. Once the specularities have \n been roughly segmented, the remaining regions of the image \n are mostly Lambertian, and can be segmented into regions, \n with each region illuminated by a different set of sources, \n in a robust way. The regions are separated by boundaries \n consisting of critical points (points where one illuminant \n is perpendicular to the normal). Our region-based recur- \n sive least-squares method is impervious to noise and miss- \n ing data. The illuminant estimation can be further reﬁned \n by making use of shadow information when available, in a \n novel, integrated single-pass estimation. The method is gen- \n eralized to objects of arbitrary known geometry, by map- \n ping their normals to a sphere. Furthermore, we introduce \n a hybrid approach that combines our method with spherical \n harmonic representations of non-directional light sources, \n when such sources are present in the scene. We demon- \n strate experimentally the accuracy of our method, both in \n detecting the number of light sources and in estimating their \n directions, by testing on synthetic and real images.","bbox":{"left":0.081882,"top":0.320182,"width":0.386035,"height":0.468208}},{"text":"edge of illuminant directions is necessary both in image \n based computer graphics, and in computer vision for shape \n reconstruction. This problem is particularly hard for diffuse \n (Lambertian) surfaces and directional light sources and can- \n not be solved using local information only. In this work we \n concentrate on directional light sources because they have \n the most pronounced effects in the appearance of a scene. \n Previous methods that estimate multiple light sources re- \n quire images of a calibration object of given shape (typ- \n ically spheres) which needs to be removed from the scene \n and might cause artifacts. Instead, our method relies on par- \n tial knowledge of the geometry of the scene and can be used \n on objects of arbitrary shape. This allows us to possibly \n use any diffuse object of the scene for illumination calibra- \n tion. In this paper we present a novel methodology that inte- \n grates information from shadows, shading and specularities \n in the presence of strong directional sources of illumina- \n tion, even when signiﬁcant non-directional sources exist in \n the scene. Specularities are detected based on sharp inten- \n sity changes. The shadow-based method utilizes brightness \n variations inside the shadows cast by the object, whereas \n the shading-based method utilizes brightness variations on \n the directly illuminated portions of the object. Here, we \n demonstrate that they can be cast in an one-pass estimation \n framework. The proposed integrated method is both more \n accurate and more general in its applicability, than any of \n the three methods applied separately. Furthermore, we in- \n troduce a hybrid approach that combines our method, with \n spherical harmonic representations of non-directional light \n sources (e.g. area sources). \n In the last few years, there has been an increased inter- \n est in estimating the reﬂectance properties and the illumi- \n nation conditions of a scene based on its images, initially \n recovering a single light source [6, 12, 34, 22]. However, \n illumination in most real scenes is more complex and it is \n very likely to have a number of co-existing light sources \n in a scene. Various types of light sources are discussed \n in [11]. Accuracy, photorealism and generality of many \n Image Based Modelling and Rendering (IMBR) applica- \n tions depends on the knowledge of illumination. A number","bbox":{"left":0.504676,"top":0.288112,"width":0.386035,"height":0.6131490000000002}},{"text":"1. Introduction \n In order to integrate seamlessly a virtual object in a real \n scene, we need to simulate accurately the interactions of the \n virtual object with the illumination of the scene. Further- \n more, to manipulate existing images realistically, knowl-","bbox":{"left":0.081882,"top":0.810506,"width":0.386034,"height":0.09075600000000006}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}