Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute,...
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Transcript of Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute,...
![Page 1: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/1.jpg)
Recovering Surface Layout from a Single Image
D. Hoiem, A.A. Efros, M. HebertRobotics Institute, CMU
Presenter: Derek HoiemCS 598, Spring 2009
Jan 29, 2009
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Why worry about 3d scenes?
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Reason 1: We may want to interact with the scene
Navigation Manipulation
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Reason 2: We need context
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Reason 2: We need context
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2D Object Detection
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What the 2D Detector Sees
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Computers need context tooTrue
Detection
True Detections
MissedMissed
False Detections
Local Detector: [Dalal-Triggs 2005]
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Context in Image Space
[Kumar Hebert 2005][Torralba Murphy Freeman 2004]
[He Zemel Cerreira-Perpiñán 2004]
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We need 3d info to reason about 3d relationships
Close
Not Close
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How to represent scene space?
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How to represent scene space?
Holistic Scene Space: “Gist”
Oliva & Torralba 2001
Torralba & Oliva 2002
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How to represent scene space?
Depth Map
Saxena, Chung & Ng 2005, 2007
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Gibson’s Surface Layout
slide from Aude Oliva
• Gibson: “The elementary impressions of a visual world are those of surface and edge.” The Perception of the Visual World (1950)• Focus on texture gradients
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Surface Layout (Gibson cont.)
slide from Aude Oliva
Gibson’s Surface Layout
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Surface Layout (Gibson cont.)
slide from Aude Oliva
Gibson’s Surface Layout
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Marr’s 2½D Sketch
Marr’s 2½-D Sketch
Figs from Aude Oliva slide
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Surface Layout (this paper)
Goal: Label image into 7 Geometric Classes:• Support• Vertical
– Planar: facing Left (), Center ( ), Right ()– Non-planar: Solid (X), Porous or wiry (O)
• Sky
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Our Main Challenge
• Recovering 3D geometry from single 2D projection
• Infinite number of possible solutions!
…
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Our World is Structured
Abstract World Our World
Image Credit (left): F. Cunin and M.J. Sailor, UCSD
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Most Early Work Tried to Manually Specify the Structure
• Hansen & Riseman 1978 (VISIONS)• Barrow & Tenenbaum 1978 (Intrinsic Images)• Brooks 1979 (ACRONYM)• Marr 1982 (2½ D Sketch)
Ohta & Kanade 1978Guzman 1968
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Learn the Structure of the World
…
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Infer Most Likely Scene
Unlikely Likely
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1. Use All Available Cues
Vanishing points, lines
Color, texture, image location
Texture gradient
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Use All Available Cues
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2. Get Good Spatial Support
50x50 Patch50x50 Patch
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Image Segmentation
• Single segmentation won’t work
• Solution: multiple segmentations
…
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…
…
For each segment:
- Get P(good segment | data) P(label | good segment, data)
Labeling Segments
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Image Labeling
…
Labeled Segmentations
Labeled Pixels
segments
datasegmentgoodlabelPdatasegmentgoodPdatalabelP ),|()|()|(
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30
…
Gray?
High inImage?
Many LongLines?
Yes
No
NoNo
No
Yes Yes
Yes
Very High Vanishing
Point?
High in Image?
Smooth? Green?
Blue?
Yes
No
NoNo
No
Yes Yes
Yes
Decision Trees + AdaboostDecision Trees + Adaboost
Ground Vertical Sky
Collins et al. 2002
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Surface Confidence Maps
P(Support) P(Vertical) P(Sky)
P(Planar Left) P(Planar Center) P(Planar Right)
P(Non-Planar Porous) P(Non-Planar Solid)
Test Image
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Experiments: Input Image
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Experiments: Ground Truth
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Experiments: Our Result
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Surface Estimates: Outdoor
Input Image Ground Truth Our Result
Avg. Accuracy
Main Class: 88%
Subclass: 62%
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Input Image Ground Truth Our Result
Surface Estimates: Outdoor
![Page 37: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/37.jpg)
Input Image Ground Truth Our Result
Surface Estimates: Outdoor
![Page 38: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/38.jpg)
Surface Estimates: Paintings
Input Image Our Result
![Page 39: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/39.jpg)
Surface Estimates: Indoor
Avg. Accuracy
Main Class: 93%
Subclass: 76%
Input Image Ground Truth Our Result
![Page 40: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/40.jpg)
Failures: Reflections and Shadows
Input Image Our Result
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Average Accuracy
Main Class: 88%
Subclasses: 61%
![Page 42: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/42.jpg)
Importance of Many Cues
All Position Only
Color Only
Texture Only
Perspective Only
Main 88% 83% 72% 80% 68%
Subclass 61% 43% 43% 55% 52%
All All But Position
All But Color
All But Texture
All But Perspective
Main 88% 84% 87% 87% 88%
Subclass 61% 60% 60% 58% 57%
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Importance of Many Cues
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Spatial Support Matters
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Automatic Photo Popup
Labeled Image Fit Ground-Vertical Boundary with Line
Segments
Form Segments into Polylines
Cut and Fold
Final Pop-up Model
[Hoiem Efros Hebert 2005]
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video
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Surfaces Not Enough – Need Occlusion Reasoning
Image Surface Labels 3D Model
![Page 48: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/48.jpg)
Surfaces + Occlusions + Objects = Better 3D Models
Surfaces Occlusions
Objects and Viewpoint
SupportHorizon, Object Maps
Surface Maps
Depth, Boundaries
Boundaries
Horizon, O
bject Maps
Viewpoint/Size Reasoning
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video 2
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Contributions• General principles
– Learn the structure of the world– Use all available cues– Spatial support matters– Use redundancy to deal with unreliable processes
(segmentation)
• Results include entire spread of failure and success
• First work to convincingly demonstrate single-view reconstruction
![Page 51: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/51.jpg)
Criticisms• Still just 2D pattern recognition?
• Not clear how to generalize to arbitrary 3d angles
• Restricted to visible portion of scene
• Coarse layout: not clear if applicable to personal space or object shapes
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Ideas for improvement
• Try improving features (e.g., add bag of words)
• Extend to characterize object shapes?
• Combine this surface-based layout with depth estimates from Saxena et al.
![Page 53: Recovering Surface Layout from a Single Image D. Hoiem, A.A. Efros, M. Hebert Robotics Institute, CMU Presenter: Derek Hoiem CS 598, Spring 2009 Jan 29,](https://reader036.fdocuments.us/reader036/viewer/2022062321/56649eae5503460f94bb5544/html5/thumbnails/53.jpg)
Discussion• Use for context (Eamon)• Multiple segmentations (Duan, Sanketh)• Subcategories (Duan, Sanketh)• Global info, use of object knowledge (Binbin)• Combination with multiview cues (Mani)• Landmarks (Gang)
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Thank you
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Things to cover when you present
• Background• Overview of method• Results• Things you like• Things you don’t• Ideas for improvement• Address bulletin board postings