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Recognition of 3D Objects or, 3D Recognition of Objects

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Presentation Transcript

Overview

- 3D object recognition was dead, now it’s coming back
- These papers are within the last 2 years
- Doesn’t really work yet, but it’s just a beginning

Papers

- The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects
- CVPR 2006
- 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation
- CVPR 2007
- 3D Generic Object Categorization, Localization and Pose Estimation
- ICCV 2007

The Layout Consistent Random Field for

Recognizing and Segmenting Partially Occluded Objects

John Winn

Microsoft Research

Cambridge

Jamie Shotton

University of

Cambridge

Introduction

- Needed to understand next paper
- It’s 2D
- What does it try to solve?
- Recognize one class of object at one pose and one scale, but with occlusions
- Does it work?
- Yes, really well, especially given occlusions

Introduction

- What is interesting about it?
- Segments objects
- Interesting methods
- No sliding windows
- Multiple instances for free

Overview

- Instead of sparse parts at features, use a densely covering part grid

[Fischler & Elschlager 73]

[Winn & Shotton 06]

Recognizing New Image – Overview

- Walk through an example

Recognizing a New Image – Overview

1. Pixels guess their part

Recognizing a New Image – Overview

2. Maximize layout consistency

Layout Consistency

- Defined pairwise between two pixels:

PI, PJ => Bool

- Means pixels I, J could be part of one instance
- Toy example:

Object: 1,2,3,4,5

Image:

2,3,4,5,0,0,1,2,3,4,5,2,3,4,5,0,0

instance 2

instance 3

occlusion

Layout Consistency- Defined pairwise between two pixels:

PI, PJ => Bool

- Means pixels I, J could be part of one instance
- Toy example:

Object: 1,2,3,4,5

Image:

2,3,4,5,0,0,1,2,3,4,5,2,3,4,5,0,0

Layout Consistency

- In 2D, consistent IFF their relative assignments could exist in a deformed regular grid
- Formally:

Overview

2. Maximize layout consistency

Layout Consistency

3. Find consistent regions; create instances

Possible due to layout inconsistency at occluding borders

Implementation Details

- Trained on manually segmented data
- Crux of algorithm is conditional distribution
- Like a probability for each possibility, or a score
- Algorithm is just finding maximum

Part Appearance

- Each pixel prefers parts that match surrounding image data
- Randomized decision trees
- Multiple trees, each trained on a subset of the data
- Node is maximal-information-gain binary test on two nearby pixels’ intensities
- Leaf of node is histogram of part possibilities
- Actual preference is average over all trees

Deformed Training Part Labelings

- Fits parts tighter

1. Label by grid

2. Learn from data

3. Apply to data

4. Set guesses as truth

5. Relearn

Part Layout

- Preference for layout consistency plus additional pairwise costs:
- Helps remove noise
- Align edges along image edges

Part Layout

- Return to toy example

Just appearance:

1,2,0,4,5,0,0,1,2,3,3,4,0,0,1,0

With layout costs:

1,2,3,4,5,0,0,1,2,3,3,4,0,0,0,0

instance 1

instance 2

Instance Layout

- Apply weak force trying to keep parts at sane positions relative to instance data (centroid, L/R flip)
- Toy example: 0,1,1,1,1,1,2,3,4,5 is bad!

Implementation

- Theoretically, finding global maximum of
- This is “MAP” estimation
- MAP = Maximum A Posteriori
- In reality, using tricks to find a local maximum
- α-expansion, annealed expansion move

Approximating MAP Estimation

- Global maximum is intractable
- α-expansion
- Start with given configuration
- For a given new label, ask each pixel: do you want to switch?
- Can be solved efficiently with graph cuts
- Repeat over all part labels
- Annealed expansion move
- Relabel grid, but offset to avoid local maxima

Results

Oh, snap!

Thoughts

- Bottom-up system is great
- No sliding windows
- Multiple instances for free
- Information about segment boundaries: occlusion vs. completion
- Reason about complete segment boundaries?

Carnegie Mellon

University

Carsten Rother

Microsoft Research Cambridge

John Winn

3D LayoutCRF for Multi-View Object Class Recognition and Segmentation

Introduction

- What does it try to solve?
- Extend LayoutCRF to be pose and scale invariant
- Does it work?
- Improvements to LayoutCRF work;3D information does little
- What is interesting about it?
- One method for combining 2D methods with a 3D framework
- The improvements to 2D are good

Overview

- Generate rough 3D model of class
- Parts created over 3D model

Overview

- Probability distribution

Refinements

- Part layout, instance layout take into account 3D position

Refinements

- New term: Instance cost

Instance Cost

- Eliminates false positives
- LayoutCRF: object-background cost
- Explain multiple groups with one instance

Refinements

- New term: Instance appearance

Instance appearance

- Learn color distribution for each instance
- Separate groups of pixels: definitely object, definitely background
- Use these to learn colors
- Apply cost to non-standard-color pixels

This would fail…

Implementation Details

- Parts are learned separately for each 45o viewing range, and for different scales
- Instance layout is also discretized by viewpoint

Results – Comparison to LCRF

- A little better(+ 8% recall)
- BUT they actually turn off 3D information for this comparison
- Better segmentation

Results – PASCAL 2006

- 61% precision-recall
- Previous best: 45%
- But, reduced test set
- Without 3D: -5%
- Without color: -5%

Thoughts

- Color, instance costs very nice
- Shoehorns LCRF into 3D without much success
- LCRF is already somewhat viewpoint-invariant: segments can stretch

University of Illinois at

Urbana-Champaign

Fei-Fei Li

Princeton University

3D Generic Object Categorization, Localization and Pose Estimation

Introduction

- What does it try to solve?
- Multiclass pose-invariant, scale-invariant object recognition
- Does it work?
- Not well. But it may be due to implementation
- Why is it interesting?
- Attempt learn actual 3D structure of an object
- Interesting data structure for 3D info

Overview – Data Structure

- Decompose object into large parts; find “canonical view”
- Relate parts by mutual appearance

Related Work – Aspect Graphs

- Represent stable views rather than parts

Aspect graph of a cube:

Image [Khoh & Kovesi, 99]

Implementation – Links

- Link from canonical PI to PJ consists of
- Matrix defines transformation to observe PJ when PI is viewed canonically
- AIJ is skew, tIJ is translation

Overview

- Learn data structure from images (unsupervised)
- Apply to new image by recognizing parts and selecting model that best accounts for their appearances

Implementation – Learning Parts

- Tricky implementation!
- Part = collection of SIFT features

For each pair of images of the same instance:

1. Find set M of shared SIFT features

2. RANSAC M to find a group of pairs that transform together

3. Group close-together parts of M into candidate parts

Background: What is RANSAC?

- Finds subset of data that is accounted for by some model; ignores outliers

1. Guess points

2. Fit model

3. Select matching points

4. Calculate error

Repeat!

RANSAC

- In our case: find points for which a homographic transformation of the points in image I yield the points in image J

Implementation – Canonical Views

- Goal: front-facing view of part
- Construct directed graph
- Direction means “more front-facing”
- Traverse to find canonical view
- How to go from pairwise-defined to graph?

Implementation

- Upshot: a collection of parts with canonical views and links

Recognizing a New Image

1. Extract SIFT features

2. Use scanning windows to get 5 best canonical part matches

3. For every pair of found parts, for each model, score how well the model accounts for their relative appearances

4. Select the model with the best score

Results

- Not stellar
- New test set
- Overfit?
- Comparison?

Thoughts

- Low performance may make it useless as a system, but the data structure is very nice
- Implementation has a lot of tricky parts
- Doesn’t seem to select great canonical parts
- I wonder if there’s a simpler way
- Are SIFT features the right choice?

Extremely Confusing Figure

- “Each dashed box indicates a particular view. A subset of the canonical parts is presented for each view. Part relationships are denoted by arrows.”

Overall Conclusions

- 3D is just starting out. Doesn’t work too well right now, but neither did MV at the beginning.
- LayoutCRF:
- Nice method to learn 2D patches
- 3D Object Categorization:
- Nice conceptual model relating 3D parts
- Possible to combine strengths of both?

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