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In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling.
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Perceptual organization and recognition of indoor scenes from rgb-d images
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Overfeat: Integrated recognition, localization and detection using convolutional networks
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Learning rich features from rgb-d images for object detection and segmentation
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Fully convolutional networks for semantic segmentation
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Geometry driven semantic labeling of indoor scenes
S. K. Hameed, M. Bennamoun, F. Sohel, and R. Togneri · 2014
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Simultaneous detection and segmentation
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Depth extraction from video using non-parametric sampling
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Discriminatively trained dense surface normal estimation
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Coarse-to-fine depth estimation from a single image via coupled regression and dictionary learning
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Shape, illumination, and reflectance from shading
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Designing deep networks for surface normal estimation
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