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Unfolding an Indoor Origami World
David Fouhey, Abhinav Gupta, Martial Hebert
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Unfolding an Indoor Origami World David Fouhey, Abhinav Gupta, - - PowerPoint PPT Presentation
Unfolding an Indoor Origami World David Fouhey, Abhinav Gupta, - - PowerPoint PPT Presentation
Unfolding an Indoor Origami World David Fouhey, Abhinav Gupta, Martial Hebert 1 2 3 Local Evidence Hoiem et al. 2005, Saxena et al. 2005, Fouhey et al. 2013, etc. 4 Constraints 5 Constraints for Single Image 3D Local Smoothness Low
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Local Evidence
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Hoiem et al. 2005, Saxena et al. 2005, Fouhey et al. 2013, etc.
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Constraints
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Constraints for Single Image 3D
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Local Smoothness
Low Level, Generic
Hoiem et al. 2005, Saxena et al. 2005, 2008, Munoz et al., 2009, etc.
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Constraints for Single Image 3D
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Local Smoothness
Low Level, Generic
Hoiem et al. 2005, Saxena et al. 2005, 2008, Munoz et al., 2009, etc.
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Constraints for Single Image 3D
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Low Level, Generic High Level, Physical
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High Level, Physical
Constraints for Single Image 3D
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Local Smoothness
Low Level, Generic
Coughlan and Yuille 2000, etc.
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High Level, Physical
Constraints for Single Image 3D
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Local Smoothness
Low Level, Generic
Hedau et al. 2009, Del Pero et al., 2011, Wang et al., 2012, Schwing et al. 2012, etc.
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High Level, Physical
Constraints for Single Image 3D
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Local Smoothness
Low Level, Generic
Lee et al. 2010, Xiao et al. 2012, Zhao et al. 2013, Schwing et al., 2013, etc.
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High Level, Physical
Constraints for Single Image 3D
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Low Level, Generic
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Mid-level in the Past
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Huffman 71, Clowes 71, Kanade 80, 81 Sugihara 86, Malik 87, etc.
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Our Mid-Level Constraints
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This Work
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Input: Single Image Output: Discrete Scene Parse
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Overview
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Parameterization Formulation Experimental Results
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Overview
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Parameterization Formulation Experimental Results
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Parameterization
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Parameterization
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vp1 vp2 vp3
VP Estimator from Hedau et al., 2009
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Parameterization
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Two VPs give grid cell
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Encoding Surface Normals
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Encoding Surface Normals
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Encoding Surface Normals
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Encoding Surface Normals
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x1,…, x400 x401,…, x800 x801,…, x1200
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Related Parameterizations
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vp1 vp3
x2 x1 x3 x4
Hedau et al., 2009; Wang et al. 2010, Schwing et al., 2012, 2013
vp2
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Overview
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Parameterization Formulation Experimental Results
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Parameterization
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Formulation
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Unaries
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Unaries
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Low c Any 3D Evidence High c
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Unaries
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Input 3D Primitive Bank
Local: Data-Driven 3D Primitives
Fouhey, Gupta, Hebert, 2013
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Unaries
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Hedau, Hoiem, Forsyth, 2009
Global: Cuboid Fit + Clutter Mask
Input Predicted Walls Clutter Mask
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Binaries
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Convex/Concave Constraints
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Convex (+) Concave (-)
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Convex/Concave Constraints
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Detected Concave (-)
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Convex/Concave Constraints
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Detected Concave (-)
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Convex/Concave Constraints
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Detected Concave (-)
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Convex/Concave Constraints
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Detected Concave (-)
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Convex/Concave Constraints
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Detected Concave (-)
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Detecting Convex/Concave
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Ground-Truth Discontinuities similar to Gupta, Arbelaez, Malik, 2013 3DP from Fouhey, Gupta, Hebert, 2013
Input 3D Primitive Bank
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Use 3DP to Transfer Discontinuities
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Smoothness
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Constraints
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Solving the Model
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Overview
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Parameterization Formulation Experimental Results
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Dataset
NYU Depth v2: 795 Train, 654 Test
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Qualitative Results
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Qualitative Results
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Qualitative Results
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Qualitative Results
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Surface Connection Graphs
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Convex Concave
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Baseline
Primary Baseline: 3D Primitives
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Fouhey, Gupta, Hebert, 2013
Output Input
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Quantitative Results
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Summary Stats (⁰) (Lower Better) % Good Pixels (Higher Better)
11.25⁰ 22.5⁰ 30⁰ Mean Median
3DP 35.9 52.0 57.8 36.0 20.5 49.4 Hedau et al. 34.2 49.3 54.4 40.0 23.5 54.1
RMSE
Lee et al. 18.6 38.6 49.9 43.3 36.3 54.6 Proposed 37.6 53.3 58.9 35.1 19.2 48.7
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Quantitative Results
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Failure Modes
Mistaken but Confident Evidence
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Failure Modes
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Missing High-Level Modeling
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Conclusions
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Single Image Parameterization Formulation Discrete Parse
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Thank You
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