Recovering surface layout from an image

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Recovering surface layout from an image (2007)

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by Derek Hoiem , Alexei A. Efros , Martial Hebert

Venue:	In IJCV
Citations:	205 - 25 self

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@INPROCEEDINGS{Hoiem07recoveringsurface,
    author = {Derek Hoiem and Alexei A. Efros and Martial Hebert},
    title = {Recovering surface layout from an image},
    booktitle = {In IJCV},
    year = {2007}
}

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Abstract

Humans have an amazing ability to instantly grasp the overall 3D structure of a scene – ground orientation, relative positions of major landmarks, etc – even from a single image. This ability is completely missing in most popular recognition algorithms, which pretend that the world is flat and/or view it through a patch-sized peephole. Yet it seems very likely that having a grasp of this “surface layout ” of a scene should be of great assistance for many tasks, including recognition, navigation, and novel view synthesis. In this paper, we take the first step towards constructing the surface layout, a labeling of the image into geometric classes. Our main insight is to learn appearance-based models of these geometric classes, which coarsely describe the 3D scene orientation of each image region. Our multiple segmentation framework provides robust spatial support, allowing a wide variety of cues (e.g., color, texture, and perspective) to contribute to the confidence in each geometric label. In experiments on a large set of outdoor images, we evaluate the impact of the individual cues and design choices in our algorithm. We further demonstrate the applicability of our method to indoor images, describe potential applications, and discuss extensions to a more complete notion of surface layout.

Keyphrases

surface layout geometric class main insight individual cue novel view synthesis design choice single image multiple segmentation framework first step towards scene ground orientation scene orientation image region great assistance robust spatial support geometric label popular recognition algorithm outdoor image patch-sized peephole large set relative position many task major landmark amazing ability appearance-based model wide variety complete notion describe potential application

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