Interactive and immersive environments which model the real world often use images of the environment to capture realistic visual complexity. Image-based modeling techniques permit the creation of visually interesting geometric models from photographs. These models are textured by resampling these images of the scene; we call this process image-based texturing. The problem with traditional image-based texturing is the poor quality of the extracted textures, which are often blurred or stretched. This paper introduces a novel technique to improve the quality of image-based texturing processes by introducing a physically-based metric that can be used to extend current texture synthesis methods. We propose a sampling-based metric of texture quality based on the Jacobian matrix of the imaging transform, which captures the interaction of the imaging system with the imaged environment. This metric suggests a physical interpretation of the multi-resolution image representations widely used in texture synthesis. Use of this metric enables synthesis of high spatial frequency detail into regions of an image-based model's textures where the imaging process captures only low frequency texture data. Given a small set of input images and a geometric model of the scene, this technique allows the creation of uniform, high-resolution textures. Our technique relieves the user of the burden of collection large numbers of images and increases the quality of user-driven image-based modeling systems. This improved quality is important in order to create compelling visual experiences in interactive environments. * Abstract Interactive and immersive environments which model the real world often use images of the environment to capture realistic visual complexity. Image-based modeling techniques permit the creation of visually interesting geometric models from photographs. These models are textured by resampling these images of the scene; we call this process image-based texturing. The problem with traditional image-based texturing is the poor quality of the extracted textures, which are often blurred or stretched. This paper introduces a novel technique to improve the quality of image-based texturing processes by introducing a physically-based metric that can be used to extend current texture synthesis methods. We propose a sampling-based metric of texture quality based on the Jacobian matrix of the imaging transform, which captures the interaction of the imaging system with the imaged environment. This metric suggests a physical interpretation of the multi-resolution image representations widely used in texture synthesis. Use of this metric enables synthesis of high spatial frequency detail into regions of an image-based model's textures where the imaging process captures only low frequency texture data. Given a small set of input images and a geometric model of the scene, this technique allows the creation of uniform, high-resolution textures. Our technique relieves the user of the burden of collection large numbers of images and incr...

Interactive and immersive environments which model the real world often use images of the environment to capture realistic visual complexity. Image-based modeling techniques permit the creation of visually interesting geometric models from photographs. These models are textured by resampling these images of the scene; we call this process image-based texturing. The problem with traditional image-based texturing is the poor quality of the extracted textures, which are often blurred or stretched.This paper introduces a novel technique to improve the quality of image-based texturing processes by introducing a physically-based metric that can be used to extend current texture synthesis methods. We propose a sampling-based metric of texture quality based on the Jacobian matrix of the imaging transform, which captures the interaction of the imaging system with the imaged environment. This metric suggests a physical interpretation of the multi-resolution image representations widely used in texture synthesis. Use of this metric enables synthesis of high spatial frequency detail into regions of an image-based model's textures where the imaging process captures only low frequency texture data. Given a small set of input images and a geometric model of the scene, this technique allows the creation of uniform, high-resolution textures. Our technique relieves the user of the burden of collection large numbers of images and increases the quality of user-driven image-based modeling systems. This improved quality is important in order to create compelling visual experiences in interactive environments. * e-mail: mccloud@graphics.cornell.edu † e-mail:kb@cs.cornell.edu ‡ e-mail:dpg@graphics. AbstractInteractive and immersive environments which model the real world often use images of the environment to capture realistic visual complexity. Image-based modeling techniques permit the creation of visually interesting geometric models from photographs. These models are textured by resampling these images of the scene; we call this process image-based texturing. The problem with traditional image-based texturing is the poor quality of the extracted textures, which are often blurred or stretched.This paper introduces a novel technique to improve the quality of image-based texturing processes by introducing a physically-based metric that can be used to extend current texture synthesis methods. We propose a sampling-based metric of texture quality based on the Jacobian matrix of the imaging transform, which captures the interaction of the imaging system with the imaged environment. This metric suggests a physical interpretation of the multi-resolution image representations widely used in texture synthesis. Use of this metric enables synthesis of high spatial frequency detail into regions of an image-based model's textures where the imaging process captures only low frequency texture data. Given a small set of input images and a geometric model of the scene, this technique allows the creation of uniform, high-resolution textures. Our tec...