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Cuboid-maps for indoor illumination modeling and augmented reality rendering

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2021-05-05
Authors/Contributors
Abstract
This thesis proposes a novel approach for indoor scene illumination modeling and augmented reality rendering. Our key observation is that an indoor scene is well represented by a set of rectangular spaces, where important illuminants reside on their boundary faces, such as a window on a wall or a ceiling light. Given a perspective image or a panorama and detected rectangular spaces as inputs, we estimate their cuboid shapes, and infer illumination components for each face of the cuboids by a simple convolutional neural architecture. The process turns an image into a set of cuboid environment maps, each of which is a simple extension of a traditional cube-map. For augmented reality rendering, we simply take a linear combination of inferred environment maps and an input image, producing surprisingly realistic illumination effects. This approach is simple and efficient, avoids flickering, and achieves quantitatively more accurate and qualitatively more realistic effects than competing substantially more complicated systems.
Document
Identifier
etd21423
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Furukawa, Yasutaka
Language
English
Member of collection
Download file Size
input_data\21573\etd21423.pdf 10.53 MB

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