The Experts below are selected from a list of 72540 Experts worldwide ranked by ideXlab platform
Katsushi Ikeuchi - One of the best experts on this subject based on the ideXlab platform.
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Fast image synthesis of virtual objects in a Real Scene with natural shadings
Systems and Computers in Japan, 2005Co-Authors: Imari Sato, Yoichi Sato, Morihiro Hayashida, Fumiyo Kai, Katsushi IkeuchiAbstract:We propose a new method for superimposing synthetic objects with natural shadings and cast shadows onto a Real Scene whose illumination condition is dynamically changing in this paper. In general, high computational cost for rendering virtual objects with convincing shading and shadows, such as interreflections or shadows under area light sources, prohibits Real-time synthesis of such composite images with superimposed virtual objects. To overcome this limitation, we take advantage of the linearity of the relationship between brightness changes observed on an object surface and change of illumination radiance in a Scene and introduce an efficient rendering technique based on a linear combination of pre-rendered reference images of the Scene. We have successfully tested the proposed method in a natural illumination condition of an indoor environment to demonstrate how fast the method could superimpose a virtual object onto the Scene with highly Realistic shadings and shadows. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(14): 102–111, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.10155
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acquiring a radiance distribution to superimpose virtual objects onto a Real Scene
Modelling from reality, 2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a Real Scene automatically and use it for superimposing virtual objects appropriately onto a Real Scene. First, a geometric model of the Scene is constructed from a pair of omni-directional images by using an omni-directional stereo algorithm. Then radiance of the Scene is computed from a sequence of omni-directional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the Real Scene. We successfully tested the proposed method by using Real images to show its effectiveness.
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Illumination distribution from shadows
2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:The image irradiance of a three-dimensional object is known to be the function of three components: the distribution of light sources, the shape, and reflectance of a Real object surface. In the past, recovering the shape and reflectance of an object surface from the recorded image brightness has been intensively investigated. On the other hand, there has been little progress in recovering illumination from the knowledge of the shape and reflectance of a Real object. In this paper, we propose a new method for estimating the illumination distribution of a Real Scene from image brightness observed on a Real object surface in that Scene. More specifically, we recover the illumination distribution of the Scene from a radiance distribution inside shadows cast by an object of known shape onto another object surface of known shape and reflectance. By using the occlusion information of incoming light, we are able to reliably estimate the illumination distribution of a Real Scene, even in a complex illumination environment.
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AMCP - A Method for Estimating Illumination Distribution of a Real Scene Based on Soft Shadows
Advanced Multimedia Content Processing, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for estimating an illumination distribution of a Real Scene. Shadows in a Real Scene are usually observed as soft shadows that do not have sharp edges. In the proposed method, illumination distribution of the Real Scene is estimated based on radiance distribution inside the soft shadows cast by an object in the Scene. By observing shadows and not illumination itself, the proposed method is able to avoid several technical problems which the previously proposed methods suffered from: how to capture a wide field of view of the entire Scene and how to capture a high dynamic range of the illumination. The estimated illumination distribution is then used for rendering virtual objects superimposed onto images of the Real Scene. We successfully tested the proposed method by using Real images to demonstrate its effectiveness.
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acquiring a radiance distribution to superimpose virtual objects onto a Real Scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a Real Scene automatically and use it for superimposing virtual objects appropriately onto a Real Scene. First, a geometric model of the Scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the Scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution mapped onto the geometric model is used for rendering virtual objects superimposed onto the Scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the Real Scene. We successfully tested the proposed method by using Real images to show its effectiveness.
Imari Sato - One of the best experts on this subject based on the ideXlab platform.
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Fast image synthesis of virtual objects in a Real Scene with natural shadings
Systems and Computers in Japan, 2005Co-Authors: Imari Sato, Yoichi Sato, Morihiro Hayashida, Fumiyo Kai, Katsushi IkeuchiAbstract:We propose a new method for superimposing synthetic objects with natural shadings and cast shadows onto a Real Scene whose illumination condition is dynamically changing in this paper. In general, high computational cost for rendering virtual objects with convincing shading and shadows, such as interreflections or shadows under area light sources, prohibits Real-time synthesis of such composite images with superimposed virtual objects. To overcome this limitation, we take advantage of the linearity of the relationship between brightness changes observed on an object surface and change of illumination radiance in a Scene and introduce an efficient rendering technique based on a linear combination of pre-rendered reference images of the Scene. We have successfully tested the proposed method in a natural illumination condition of an indoor environment to demonstrate how fast the method could superimpose a virtual object onto the Scene with highly Realistic shadings and shadows. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(14): 102–111, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.10155
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acquiring a radiance distribution to superimpose virtual objects onto a Real Scene
Modelling from reality, 2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a Real Scene automatically and use it for superimposing virtual objects appropriately onto a Real Scene. First, a geometric model of the Scene is constructed from a pair of omni-directional images by using an omni-directional stereo algorithm. Then radiance of the Scene is computed from a sequence of omni-directional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the Real Scene. We successfully tested the proposed method by using Real images to show its effectiveness.
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Illumination distribution from shadows
2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:The image irradiance of a three-dimensional object is known to be the function of three components: the distribution of light sources, the shape, and reflectance of a Real object surface. In the past, recovering the shape and reflectance of an object surface from the recorded image brightness has been intensively investigated. On the other hand, there has been little progress in recovering illumination from the knowledge of the shape and reflectance of a Real object. In this paper, we propose a new method for estimating the illumination distribution of a Real Scene from image brightness observed on a Real object surface in that Scene. More specifically, we recover the illumination distribution of the Scene from a radiance distribution inside shadows cast by an object of known shape onto another object surface of known shape and reflectance. By using the occlusion information of incoming light, we are able to reliably estimate the illumination distribution of a Real Scene, even in a complex illumination environment.
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AMCP - A Method for Estimating Illumination Distribution of a Real Scene Based on Soft Shadows
Advanced Multimedia Content Processing, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for estimating an illumination distribution of a Real Scene. Shadows in a Real Scene are usually observed as soft shadows that do not have sharp edges. In the proposed method, illumination distribution of the Real Scene is estimated based on radiance distribution inside the soft shadows cast by an object in the Scene. By observing shadows and not illumination itself, the proposed method is able to avoid several technical problems which the previously proposed methods suffered from: how to capture a wide field of view of the entire Scene and how to capture a high dynamic range of the illumination. The estimated illumination distribution is then used for rendering virtual objects superimposed onto images of the Real Scene. We successfully tested the proposed method by using Real images to demonstrate its effectiveness.
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acquiring a radiance distribution to superimpose virtual objects onto a Real Scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a Real Scene automatically and use it for superimposing virtual objects appropriately onto a Real Scene. First, a geometric model of the Scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the Scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution mapped onto the geometric model is used for rendering virtual objects superimposed onto the Scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the Real Scene. We successfully tested the proposed method by using Real images to show its effectiveness.
Yoichi Sato - One of the best experts on this subject based on the ideXlab platform.
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Fast image synthesis of virtual objects in a Real Scene with natural shadings
Systems and Computers in Japan, 2005Co-Authors: Imari Sato, Yoichi Sato, Morihiro Hayashida, Fumiyo Kai, Katsushi IkeuchiAbstract:We propose a new method for superimposing synthetic objects with natural shadings and cast shadows onto a Real Scene whose illumination condition is dynamically changing in this paper. In general, high computational cost for rendering virtual objects with convincing shading and shadows, such as interreflections or shadows under area light sources, prohibits Real-time synthesis of such composite images with superimposed virtual objects. To overcome this limitation, we take advantage of the linearity of the relationship between brightness changes observed on an object surface and change of illumination radiance in a Scene and introduce an efficient rendering technique based on a linear combination of pre-rendered reference images of the Scene. We have successfully tested the proposed method in a natural illumination condition of an indoor environment to demonstrate how fast the method could superimpose a virtual object onto the Scene with highly Realistic shadings and shadows. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(14): 102–111, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.10155
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acquiring a radiance distribution to superimpose virtual objects onto a Real Scene
Modelling from reality, 2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a Real Scene automatically and use it for superimposing virtual objects appropriately onto a Real Scene. First, a geometric model of the Scene is constructed from a pair of omni-directional images by using an omni-directional stereo algorithm. Then radiance of the Scene is computed from a sequence of omni-directional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the Real Scene. We successfully tested the proposed method by using Real images to show its effectiveness.
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Illumination distribution from shadows
2001Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:The image irradiance of a three-dimensional object is known to be the function of three components: the distribution of light sources, the shape, and reflectance of a Real object surface. In the past, recovering the shape and reflectance of an object surface from the recorded image brightness has been intensively investigated. On the other hand, there has been little progress in recovering illumination from the knowledge of the shape and reflectance of a Real object. In this paper, we propose a new method for estimating the illumination distribution of a Real Scene from image brightness observed on a Real object surface in that Scene. More specifically, we recover the illumination distribution of the Scene from a radiance distribution inside shadows cast by an object of known shape onto another object surface of known shape and reflectance. By using the occlusion information of incoming light, we are able to reliably estimate the illumination distribution of a Real Scene, even in a complex illumination environment.
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AMCP - A Method for Estimating Illumination Distribution of a Real Scene Based on Soft Shadows
Advanced Multimedia Content Processing, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for estimating an illumination distribution of a Real Scene. Shadows in a Real Scene are usually observed as soft shadows that do not have sharp edges. In the proposed method, illumination distribution of the Real Scene is estimated based on radiance distribution inside the soft shadows cast by an object in the Scene. By observing shadows and not illumination itself, the proposed method is able to avoid several technical problems which the previously proposed methods suffered from: how to capture a wide field of view of the entire Scene and how to capture a high dynamic range of the illumination. The estimated illumination distribution is then used for rendering virtual objects superimposed onto images of the Real Scene. We successfully tested the proposed method by using Real images to demonstrate its effectiveness.
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acquiring a radiance distribution to superimpose virtual objects onto a Real Scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Imari Sato, Yoichi Sato, Katsushi IkeuchiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a Real Scene automatically and use it for superimposing virtual objects appropriately onto a Real Scene. First, a geometric model of the Scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the Scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution mapped onto the geometric model is used for rendering virtual objects superimposed onto the Scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the Real Scene. We successfully tested the proposed method by using Real images to show its effectiveness.
Wenxiu Sun - One of the best experts on this subject based on the ideXlab platform.
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Exploiting Raw Images for Real-Scene Super-Resolution.
IEEE transactions on pattern analysis and machine intelligence, 2020Co-Authors: Wenxiu Sun, Ming-hsuan YangAbstract:Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits their applications in Real scenarios. In this paper, we study the problem of Real-Scene single image super-resolution to bridge the gap between synthetic data and Real captured images. We focus on two issues of existing super-resolution algorithms: lack of Realistic training data and insufficient utilization of visual information obtained from cameras. To address the first issue, we propose a method to generate more Realistic training data by mimicking the imaging process of digital cameras. For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images. In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction. Our model is able to generalize to different cameras without deliberately training on images from specific camera types. Extensive experiments demonstrate that the proposed algorithm can recover fine details and clear structures, and achieve high-quality results for single image super-resolution in Real Scenes.
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Towards Real Scene Super-Resolution with Raw Images
arXiv: Image and Video Processing, 2019Co-Authors: Wenxiu SunAbstract:Most existing super-resolution methods do not perform well in Real scenarios due to lack of Realistic training data and information loss of the model input. To solve the first problem, we propose a new pipeline to generate Realistic training data by simulating the imaging process of digital cameras. And to remedy the information loss of the input, we develop a dual convolutional neural network to exploit the originally captured radiance information in raw images. In addition, we propose to learn a spatially-variant color transformation which helps more effective color corrections. Extensive experiments demonstrate that super-resolution with raw data helps recover fine details and clear structures, and more importantly, the proposed network and data generation pipeline achieve superior results for single image super-resolution in Real scenarios.
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CVPR - Towards Real Scene Super-Resolution With Raw Images
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Co-Authors: Wenxiu SunAbstract:Most existing super-resolution methods do not perform well in Real scenarios due to lack of Realistic training data and information loss of the model input. To solve the first problem, we propose a new pipeline to generate Realistic training data by simulating the imaging process of digital cameras. And to remedy the information loss of the input, we develop a dual convolutional neural network to exploit the originally captured radiance information in raw images. In addition, we propose to learn a spatially-variant color transformation which helps more effective color corrections. Extensive experiments demonstrate that super-resolution with raw data helps recover fine details and clear structures, and more importantly, the proposed network and data generation pipeline achieve superior results for single image super-resolution in Real scenarios.
Ikeuchikatsushi - One of the best experts on this subject based on the ideXlab platform.
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Acquiring a Radiance Distribution to Superimpose Virtual Objects onto a Real Scene
IEEE Transactions on Visualization and Computer Graphics, 1999Co-Authors: Satoimari, Satoyoichi, IkeuchikatsushiAbstract:This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a Real Scene. Unlike the previously proposed methods, our method can measure a radiance di...
