This paper proposes a novel high-resolution multi-view dataset of complex multi-illuminant scenes... more This paper proposes a novel high-resolution multi-view dataset of complex multi-illuminant scenes with precise reflectance and shading ground-truth as well as raw depth and 3D point cloud. Our dataset challenges the intrinsic image methods by providing complex coloured cast shadows, highly textured and colourful surfaces, and specularity. This is the first publicly available multi-view real-photo dataset at such complexity with pixel-wise intrinsic ground-truth. In the effort to help evaluating different intrinsic image methods, we propose a new perception-inspired metric based on the reflectance consistency. We provide the evaluation of three intrinsic image methods using our dataset and metric.
Most existing color constancy algorithms assume uniform illumination. However, in real-world scen... more Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a Conditional Random Field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel dataset of twodominant-illuminants images comprised of laboratory, indoor and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple datasets. Experimental results show that our framework clearly outperforms single illuminant estimators, as well as a recently proposed multi-illuminant estimation approach.
2015 IEEE International Conference on Computer Vision (ICCV), 2015
In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image... more In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.
2013 IEEE International Conference on Image Processing, 2013
Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essentia... more Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.
The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in ... more The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.
ABSTRACT More realistic and accurate models of the scene illumination and object reflectance can ... more ABSTRACT More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information.
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011
The color and distribution of illuminants can significantly alter the appearance of a scene. The ... more The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of realworld illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.
2011 International Conference on Computer Vision, 2011
Object recoloring is one of the most popular photoediting tasks. The problem of object recoloring... more Object recoloring is one of the most popular photoediting tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects.
Most of the computational color constancy approaches are based on the assumption of a uniform ill... more Most of the computational color constancy approaches are based on the assumption of a uniform illumination in the scene which is not the case in many real world scenarios. A crucial ingredient in developing color constancy algorithms which can handle these scenarios is a dataset of such images with accurate illumination ground truth to be used both for estimating the parameters and for evaluating the performance. Such datasets are rare due to the complexity of the procedure involved in capturing them. To this end, we provide a framework for capturing such dataset and propose our multi-illuminant scene image dataset with pixel-wise accurate ground truth. Our dataset consists of 6 different scenes under 5 illumination conditions provided by two or three distinctly colored illuminants. The scenes are made up of complex colored objects presenting diffuse and specular reflections. We present quantitative evaluation of the accuracy of our proposed ground truth and show that the effect of ambient light is negligible.
Most existing color constancy algorithms assume uniform illumination. However, in real-world scen... more Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a Conditional Random Field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel dataset of twodominant-illuminants images comprised of laboratory, indoor and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple datasets. Experimental results show that our framework clearly outperforms single illuminant estimators, as well as a recently proposed multi-illuminant estimation approach.
The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in ... more The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.
This paper proposes a novel high-resolution multi-view dataset of complex multi-illuminant scenes... more This paper proposes a novel high-resolution multi-view dataset of complex multi-illuminant scenes with precise reflectance and shading ground-truth as well as raw depth and 3D point cloud. Our dataset challenges the intrinsic image methods by providing complex coloured cast shadows, highly textured and colourful surfaces, and specularity. This is the first publicly available multi-view real-photo dataset at such complexity with pixel-wise intrinsic ground-truth. In the effort to help evaluating different intrinsic image methods, we propose a new perception-inspired metric based on the reflectance consistency. We provide the evaluation of three intrinsic image methods using our dataset and metric.
Most existing color constancy algorithms assume uniform illumination. However, in real-world scen... more Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a Conditional Random Field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel dataset of twodominant-illuminants images comprised of laboratory, indoor and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple datasets. Experimental results show that our framework clearly outperforms single illuminant estimators, as well as a recently proposed multi-illuminant estimation approach.
2015 IEEE International Conference on Computer Vision (ICCV), 2015
In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image... more In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.
2013 IEEE International Conference on Image Processing, 2013
Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essentia... more Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.
The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in ... more The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.
ABSTRACT More realistic and accurate models of the scene illumination and object reflectance can ... more ABSTRACT More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information.
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011
The color and distribution of illuminants can significantly alter the appearance of a scene. The ... more The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of realworld illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.
2011 International Conference on Computer Vision, 2011
Object recoloring is one of the most popular photoediting tasks. The problem of object recoloring... more Object recoloring is one of the most popular photoediting tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects.
Most of the computational color constancy approaches are based on the assumption of a uniform ill... more Most of the computational color constancy approaches are based on the assumption of a uniform illumination in the scene which is not the case in many real world scenarios. A crucial ingredient in developing color constancy algorithms which can handle these scenarios is a dataset of such images with accurate illumination ground truth to be used both for estimating the parameters and for evaluating the performance. Such datasets are rare due to the complexity of the procedure involved in capturing them. To this end, we provide a framework for capturing such dataset and propose our multi-illuminant scene image dataset with pixel-wise accurate ground truth. Our dataset consists of 6 different scenes under 5 illumination conditions provided by two or three distinctly colored illuminants. The scenes are made up of complex colored objects presenting diffuse and specular reflections. We present quantitative evaluation of the accuracy of our proposed ground truth and show that the effect of ambient light is negligible.
Most existing color constancy algorithms assume uniform illumination. However, in real-world scen... more Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a Conditional Random Field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel dataset of twodominant-illuminants images comprised of laboratory, indoor and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple datasets. Experimental results show that our framework clearly outperforms single illuminant estimators, as well as a recently proposed multi-illuminant estimation approach.
The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in ... more The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.
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Papers by Shida Beigpour