Recognition of 3D Objectsor, 3D Recognition of Objects

Alec Rivers

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 forRecognizing and Segmenting Partially Occluded

Objects

John WinnMicrosoft Research

Cambridge

Jamie ShottonUniversity ofCambridge

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: 1,2,3,4,5Image:

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

Layout Consistency

• Defined pairwise between two pixels:PI, PJ => Bool

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

Object: 1,2,3,4,5Image:

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

occlusion

instance 2 instance 3instance 1

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

Overview1. Pixels guess parts2. Maximize layout consistency3. Create instances

[Winn & Shotton 06]

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 tighter1. Label by grid2. Learn from data3. Apply to data4. Set guesses as

truth5. Relearn

Part Layout• Preference for layout consistency plus additional

pairwise costs:

• Helps remove noise• Align edges along image edges

Part Layout

• Return to toy exampleJust appearance:

1,2,0,4,5,0,0,1,2,3,3,4,0,0,1,0With layout costs:

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

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

Results

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?

John Winn

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

Derek HoiemCarnegie Mellon

University

Carsten Rother Microsoft Research

Cambridge

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

3D Generic Object Categorization, Localization and Pose Estimation

Silvio SavareseUniversity of Illinois at

Urbana-Champaign

Fei-Fei LiPrinceton University

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

Image [Khoh & Kovesi, 99]

Aspect graph of a cube:

Data Structure for Cube

Left Front Right

Bottom

Top

Back

Related Work

• Constellation models

• Similar, but wraps around in 3D

vs.

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

Implementation – Links

HIJ

Part Jcanonical view

Part Icanonical view

Implementation – Links

Part Jcanonical view

HJI

Part Icanonical view

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 featuresFor each pair of images of the same

instance:1. Find set M of shared SIFT features2. RANSAC M to find a group of pairs

that transform together3. 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 points2. Fit model3. Select matching points4. Calculate errorRepeat!

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 features2. Use scanning windows to get 5 best canonical

part matches3. 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?

Results

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?