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Cornell University Program of Computer Graphics
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Detail synthesis for image-based texturing.

Ryan M. Ismert, Kavita Bala, and Donald P. Greenberg.

Technical report PCG-03-01, Program of Computer Graphics, Cornell University, Jan 2003.

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.

This paper is available as a PDF file IBG03b.pdf (610K).

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