Joint 3d scene reconstruction and class segmentation

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Joint 3d scene reconstruction and class segmentation (2013)

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by Christopher Zach , Andrea Cohen , Marc Pollefeys

Venue:	In CVPR
Citations:	18 - 4 self

BibTeX

@INPROCEEDINGS{Zach13joint3d,
    author = {Christopher Zach and Andrea Cohen and Marc Pollefeys},
    title = {Joint 3d scene reconstruction and class segmentation},
    booktitle = {In CVPR},
    year = {2013}
}

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Abstract

Both image segmentation and dense 3D modeling from images represent an intrinsically ill-posed problem. Strong regularizers are therefore required to constrain the solutions from being ’too noisy’. Unfortunately, these priors generally yield overly smooth reconstructions and/or segmentations in certain regions whereas they fail in other areas to con-strain the solution sufficiently. In this paper we argue that image segmentation and dense 3D reconstruction contribute valuable information to each other’s task. As a consequence, we propose a rigorous mathematical framework to formu-late and solve a joint segmentation and dense reconstruction problem. Image segmentations provide geometric cues about which surface orientations are more likely to appear at a certain location in space whereas a dense 3D reconstruction yields a suitable regularization for the segmentation problem by lifting the labeling from 2D images to 3D space. We show how appearance-based cues and 3D surface orientation pri-ors can be learned from training data and subsequently used for class-specific regularization. Experimental results on several real data sets highlight the advantages of our joint formulation. 1.

Keyphrases

image segmentation class segmentation scene reconstruction joint segmentation appearance-based cue surface orientation surface orientation pri-ors ill-posed problem certain region rigorous mathematical framework reconstruction yield geometric cue joint formulation segmentation problem class-specific regularization reconstruction contribute valuable information strong regularizers several real data set certain location dense reconstruction problem suitable regularization smooth reconstruction experimental result

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