A comparison of stereo correspondence algorithms can be conducted by a quantitative evaluation of disparity maps. Among the existing evaluation methodologies, the Middlebury’s methodology is commonly used. However, the Middlebury’s methodology has shortcomings in the evaluation model and the error measure. These shortcomings may bias the evaluation results, and make a fair judgment about algorithms accuracy difficult. An alternative, the methodology is based on a multiobjective optimisation model that only provides a subset of algorithms with comparable accuracy. In this paper, a quantitative evaluation of disparity maps is proposed. It performs an exhaustive assessment of the entire set of algorithms. As innovative aspect, evaluation results are shown and analysed as disjoint groups of stereo correspondence algorithms with comparable accuracy. This innovation is obtained by a partitioning and grouping algorithm. On the other hand, the used error measure offers advantages over the error measure used in the Middlebury’s methodology. The experimental validation is based on the Middlebury’s test-bed and algorithms repository. The obtained results show seven groups with different accuracies. Moreover, the top-ranked stereo correspondence algorithms by the Middlebury’s methodology are not necessarily the most accurate in the proposed methodology

1. An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian
ivan.cabezas@correounivalle.edu.co
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome - Italy

2. Multimedia and Vision Laboratory
 MMV is a research group of the Universidad del Valle in Cali, Colombia
Ivan Maria et al.
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 2

3. Ayax Inc.
 Ayax Inc. offers informatics solutions for decision analysis
Margaret
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 3

4. Content
 Stereo Vision
 Canonical Stereo Geometry and Disparity
 Ground-truth Based Evaluation
 Quantitative Evaluation Methodologies
 Middlebury’s Methodology
 A* Methodology
 A* Groups Methodology
 Experimental Results
 Final Remarks
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 4

5. Stereo Vision
 The stereo vision problem is to recover the 3D structure of the scene using
two or more images
3D World
Optics
Problem
Camera Inverse
System Problem
Disparity Map Reconstruction
Algorithm
2D Images
Left Right
Correspondence
Stereo Images Algorithm 3D Model
Yang Q. et al., Stereo Matching with Colour-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling, IEEE PAMI 2009
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 5

6. Canonical Stereo Geometry and Disparity
 Disparity is the distance between corresponding points
Accurate Estimation Inaccurate Estimation
P P
P’
Z Z’
pl pr pl pr
πl πr πl πr
pr ’
f f
Cl B Cr Cl B Cr
Trucco, E. and Verri A., Introductory Techniques for 3D Computer Vision, Prentice Hall 1998
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 6

7. Ground-truth Based Evaluation
 Ground-truth based evaluation is based on the comparison using disparity
ground-truth data
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Tola, E., Lepetit, V. and Fua, P., A Fast Local Descriptor for Dense Matching, CVPR 2008
Strecha, C., et al. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery, CVPR 2008
http://www.zf-usa.com/products/3d-laser-scanners/
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 7

8. Quantitative Evaluation Methodologies
 The use of a methodology allows to:
 Assert specific components and procedures
 Tune algorithm's parameters
 Support decision for researchers and
practitioners
 Measure the progress on the field
Szeliski, R., Prediction Error as a Quality Metric for Motion and Stereo, ICCV 2000
Kostliva, J., Cech, J., and Sara, R., Feasibility Boundary in Dense and Semi-Dense Stereo Matching, CVPR 2007
Tomabari, F., Mattoccia, S., and Di Stefano, L., Stereo for robots: Quantitative Evaluation of Efficient and Low-memory Dense Stereo Algorithms, ICCARV 2010
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 8

9. Middlebury’s Methodology
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 9

10. Middlebury’s Methodology (ii)
Select and Apply Stereo Algorithms Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 2.20 1 6.99 2 6.36 1
GC+SegmBorder 4.99 6 5.78 1 8.66 5
Compute Error Measures PUTv3 2.40 2 9.11 6 6.56 2
PatchMatch 2.47 3 7.80 3 7.11 3
ImproveSubPix 2.96 4 8.22 4 8.55 4
Algorithm nonocc all disc
OverSegmBP 3.19 5 8.81 5 8.89 6
ObjectStereo 2.20 6.99 6.36
GC+SegmBorder 4.99 5.78 8.66
PUTv3 2.40 9.11 6.56 Algorithm Average Final
PatchMatch 2.47 7.80 7.11 Rank Ranking
ImproveSubPix 2.96 8.22 8.55 ObjectStereo 1.33 1
OverSegmBP 3.19 8.81 8.89 PatchMatch 3.00 2
PUTv3 3.33 3
GC+SegmBorder 4.00 4
ImproveSubPix 4.00 5
OverSegmBP 5.33 6
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 10

11. Middlebury’s Methodology (iii)
Apply Evaluation Model Interpret Results
The ObjectStereo algorithm
produces accurate results
Middlebury’s
Evaluation Model
Algorithm Average Final
Rank Ranking
ObjectStereo 1.33 1
PatchMatch 3.00 2
PUTv3 3.33 3
GC+SegmBorder 4.00 4
ImproveSubPix 4.00 5
OverSegmBP 5.33 6
Scharstein, D. and Szeliski, R., A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, IJCV 2002
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 11

12. Middlebury’s Methodology (iv): Weaknesses
 The Middlebury’s evaluation model have some shortcomings
 In some cases, the ranks are assigned arbitrarily
 The same average ranking does not imply the same performance (and
vice versa)
 The cardinality of the set of top-performer algorithms is a free parameter
 It operates values related to incommensurable measures
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 12

13. Middlebury’s Methodology (v): Weaknesses
 The BMP percentage measures the quantity of disparity estimation errors
exceeding a threshold
 The BMP measure have some shortcomings:
 It is sensitive to the threshold selection
 It ignores the error magnitude
 It ignores the inverse relation between depth and disparity
 It may conceal estimation errors of a large magnitude, and, also it may
penalise errors of small impact in the final 3D reconstruction
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
Gallup, D., et al. Variable Baseline/Resolution Stereo, CVPR, 2008
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 13

14. A* Methodology
 The A* evaluation methodology brings a theoretical background for the
comparison of stereo correspondence algorithms
 The set of algorithms under evaluation
 The set of estimated maps to be compared
 The function that produces a vector of error measures
 The set of vectors of error measures
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 14

15. A* Methodology (ii)
 The evaluation model of the A* methodology addresses the comparison of
stereo correspondence algorithms as a multi-objective optimisation problem
 It defines a partition over the set A (the decision space)
 Subject to:
 where ≺ denotes the Pareto Dominance relation:
Let p and q be two algorithms
Let Vp and Vq be a pair of vectors belonging to the objective space
Thus, three possible relations are considered
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 15

16. A* Methodology (iii): Pareto Dominance
 The Pareto Dominance defines a partial order relation
VGC+SegmBorder = < 4.99, 5.78, 8.66 >
VPatchMatch = < 2.47, 7.80, 7.11 >
VImproveSubPix = < 2.96, 8.22, 8.55 >
VGC+SegmBorder VPatchMatch
< 4.99, 5.78, 8.66 > < 2.47, 7.80, 7.11 >
GC+SegmBorder ~ PatchMatch
VPatchMatch VImproveSubPix
< 2.47, 7.80, 7.11 > < 2.96, 8.22, 8.55 >
Patchmatch ≺ ImproveSubPix
Van Veldhuizen, D., et al., Considerations in Engineering Parallel Multi-objective Evolutionary Algorithms, Trans in Evolutionary Computing 2003
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 16

17. A* Methodology (iv): Illustration
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
Scharstein, D. and Szeliski, R., High-accuracy Stereo Depth Maps using Structured Light, CVPR 2003
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 17

18. A* Methodology (v): Illustration
 The evaluation model performs the partitioning and the grouping of stereo
algorithms under evaluation, based on the Pareto Dominance relation
Compute Error Measures Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 2.20 6.99 6.36
ObjectStereo , GC+SegmBorder
GC+SegmBorder 4.99 5.78 8.66
PUTv3 2.40 9.11 6.56 PatchMatch , , ,
PUTv3 ImproveSubPix OverSegmBP
PatchMatch 2.47 7.80 7.11
ImproveSubPix 2.96 8.22 8.55 Algorithm nonocc all disc Set
OverSegmBP 3.19 8.81 8.89 ObjectStereo 2.20 6.99 6.36 A*
GC+SegmBorder 4.99 5.78 8.66 A*
PUTv3 2.40 9.11 6.56 A’
PatchMatch 2.47 7.80 7.11 A’
ImproveSubPix 2.96 8.22 8.55 A’
OverSegmBP 3.19 8.81 8.89 A’
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 18

19. A* Methodology (vi): Illustration
 Interpretation of results is based on the cardinality of the set A*
Apply Evaluation Model Interpret Results
A* Evaluation Model
The Objectstereo and the
GC+SegmBorder algorithms are,
Algorithm nonocc all disc Set comparable among them, and have a
ObjectStereo 2.20 6.99 6.36 A* superior performance to the rest of
algorithms
GC+SegmBorder 4.99 5.78 8.66 A*
PUTv3 2.40 9.11 6.56 A’
PatchMatch 2.47 7.80 7.11 A’
ImproveSubPix 2.96 8.22 8.55 A’
OverSegmBP 3.19 8.81 8.89 A’
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 19

20. A* Methodology (vii): Strength and Weakness
 Strength: It allows a formal interpretation of results, based on the cardinality
of the set A*, and in regard to considered imagery test-bed
 Weakness: It does not allow an exhaustive evaluation of the entire set of
algorithms under evaluation
 It computes the set A* just once, and does not bring information about A’
Cabezas, I. and Trujillo M., A Non-Linear Quantitative Evaluation Approach for Disparity Estimation, VISAPP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 20

21. A* Groups Methodology
 It extends the evaluation model of the A* methodology, incorporating the
capability of performing an exhaustive evaluation
subject to:
 It introduces the partitioningAndGrouping algorithm
A = Set ( { } );
A.load( “Algorithms.dat” );
A* = Set ( { } );
A’ = Set ( { } );
group = 1;
do {
computePartition( A, A*, A’, g, ≺ );
A*.save ( “A*_group_”+group );
group++;
A.update ( A’ ); // A = A / A*
A*.removeAll ( ); // A* = { }
A’.removeAll ( ); // A’ = { }
}while ( ! A.isEmpty ( ) );
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 21

22. A* Groups Methodology (ii): Sigma-Z-Error
 The A* Groups methodology uses the Sigma-Z-Error
(SZE) measure
 The SZE measure has the following properties:
 It is inherently related to depth reconstruction in a stereo system
 It is based on the inverse relation between depth and disparity
 It considers the magnitude of the estimation error
 It is threshold free
Cabezas, I., Padilla, V., and Trujillo M., A Measure for Accuracy Disparity Maps Evaluation, CIARP 2011
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 22

23. A* Groups Methodology (iii): Illustration
 The evaluation process of selected algorithms by using the proposal
Select Test Bed Images Select Error Criteria
nonocc all disc
Select and Apply Stereo Algorithms Select Error Measures
ObjectStereo GC+SegmBorder PUTv3
Compute Error Measures
PatchMatch ImproveSubPix OverSegmBP
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 23

24. A* Groups Methodology (iv): Illustration
 The evaluation model performs the partitioning and the grouping of stereo
algorithms under evaluation, based on the Pareto Dominance relation
Compute Error Measures Apply Evaluation Model
Algorithm nonocc all disc
ObjectStereo 73.88 117.90 36.25
GC+SegmBorder ,, PatchMatch
GC+SegmBorder 50.48 64.90 24.33
PUTv3 99.67 333.37 53.79 ObjectStereo , PUTv3 , ImproveSubPix , OverSegmBP
PatchMatch 49.95 261.84 32.85
Algorithm nonocc all disc Group
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58 GC+SegmBorder 50.48 64.90 24.33 1
PatchMatch 49.95 261.84 32.85 1
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01
OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 24

25. A* Groups Methodology (v): Illustration
Apply Evaluation Model
ObjectStereo , PUTv3, ImproveSubPix , OverSegmBP
Algorithm nonocc all disc
ObjectStereo, PUTv3 , OverSegmBP
PUTv3 99.67 333.37 53.79 Algorithm nonocc all disc
ImproveSubPix 50.66 97.94 32.01 PUTv3 99.67 333.37 53.79
OverSegmBP 58.65 108.60 34.58 OverSegmBP 58.65 108.60 34.58
ObjectStereo 73.88 117.90 36.25 ObjectStereo 73.88 117.90 36.25
OverSegmBP
ImproveSubPix
PUTv3 , ObjectStereo
ObjectStereo , PUTv3 , OverSegmBP
Algorithm nonocc all disc Group
OverSegmBP 58.65 108.60 34.58 3
Algorithm nonocc all disc Group
PUTv3 99.67 333.37 53.79
ImproveSubPix 50.66 97.94 32.01 2
ObjectStereo 73.88 117.90 36.25
PUTv3 99.67 333.37 53.79
ObjectStereo 73.88 117.90 36.25
OverSegmBP 58.65 108.60 34.58
And so on …
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 25

26. A* Groups Methodology (vi): Illustration
 Interpretation of results is based on the cardinality of each group
Apply Evaluation Model Interpret Results
A* Groups
Evaluation Model
There are 5 groups of different performance
The GC+SegmBorder and the PatchMatch
algorithms are, comparable among them,
and have a superior performance to the rest
Algorithm nonocc all disc Group of algorithms
GC+SegmBorder 50.48 64.90 24.33 1
The ImproveSubPix algorithm is superior to
PatchMatch 49.95 261.84 32.85 1 the OverSegmBP, the ObjectStereo, and
ImproveSubPix 50.66 97.94 32.01 2 the PUTv3 algorithms
OverSegmBP 58.65 108.60 34.58 3
…
ObjectStereo 73.88 117.90 36.25 4
PUTv3 99.67 333.37 53.79 5 The PUTv3 algorithm has the lowest
performance
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 26

27. Experimental Results
 The conducted evaluation involves the following elements:
Test Bed Images
Error Criteria nonocc , all , disc
Error Measures SZE , BMP
Stereo Algorithms 112 algorithms from the Middlebury’s repository
Evaluation Models A* Groups Middlebury
Scharstein, D. and Szeliski, R., http://vision.middlebury.edu/stereo/eval/, 2012
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 27

28. Experimental Results (ii)
Algorithm Group Middlebury’s
Ranking
ADCensus 2 1
AdaptingBP 2 2
CoopRegion 2 3
DoubleBP 1 4
RDP 2 5
OutlierConf 2 6
Algorithm Strategy Group Middlebury’s SubPixDoubleBP 2 7
Ranking SurfaceStereo 2 8
DoubleBP Global 1 4 WarpMat 2 9
PatchMatch Local 1 11 ObjectStereo 2 10
GC+SegmBorder Global 1 13 PatchMatch 1 11
FeatureGC Global 1 18 Undr+OverSeg 2 12
Segm+Visib Global 1 29 GC+SegmBorder 1 13
MultiresGC Global 1 30 InfoPermeable 2 14
DistinctSM Local 1 34 CostFilter 2 15
GC+occ Global 1 67
MultiCamGC Global 1 68
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 28

29. Final Remarks
 The use of the A* Groups methodology allows to perform an exhaustive
evaluation, as well as an objective interpretation of results
 Innovative results in regard to the comparison of stereo correspondence
algorithms were obtained using proposed methodology and the SZE error
measure
 The introduced methodology offers advantages over the conventional
approaches to compare stereo correspondence algorithms
 Authors are already working in order to provide to the research community an
accessible way to use the introduced methodology
An Evaluation Methodology for Stereo Correspondence Algorithms, VISAPP 2012, Rome - Italy Slide 29

30. An Evaluation Methodology for
Stereo Correspondence Algorithms
Ivan Cabezas, Maria Trujillo and Margaret Florian
ivan.cabezas@correounivalle.edu.co
February 25th 2012
International Conference on Computer Vision Theory and Applications, VISAPP 2012, Rome, Italy