mirror of
https://github.com/opencv/opencv.git
synced 2024-11-27 20:50:25 +08:00
Merge pull request #24465 from ivashmak:fix_usac_tutorial
Reformat USAC tutorial
This commit is contained in:
Alexander Smorkalov
GitHub
commit
5b0ad6b5e8
@ -1385,3 +1385,85 @@
|
||||
YEAR = {2016},
|
||||
MONTH = {October},
|
||||
}
|
||||
@inproceedings{BarathGCRANSAC,
|
||||
author = {Barath, Daniel and Matas, Jiri},
|
||||
title = {Graph-Cut RANSAC},
|
||||
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
month = {June},
|
||||
year = {2018}
|
||||
}
|
||||
@misc{barath2019progressive,
|
||||
title={Progressive NAPSAC: sampling from gradually growing neighborhoods},
|
||||
author={Barath, Daniel and Ivashechkin, Maksym and Matas, Jiri},
|
||||
year={2019},
|
||||
eprint={1906.02295},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
@inproceedings{BarathMAGSAC,
|
||||
author = {Barath, Daniel and Noskova, Jana and Ivashechkin, Maksym and Matas, Jiri},
|
||||
title = {MAGSAC++, a Fast, Reliable and Accurate Robust Estimator},
|
||||
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
month = {June},
|
||||
year = {2020}
|
||||
}
|
||||
@inproceedings{ChumPROSAC,
|
||||
title = {Matching with {PROSAC} - Progressive Sampling Consensus},
|
||||
author = {Chum, Ondrej and Matas, Jiri},
|
||||
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year = {2005}
|
||||
}
|
||||
@inproceedings{ChumLORANSAC,
|
||||
title = {Locally Optimized {RANSAC}},
|
||||
author = {Chum, Ondrej and Matas, Jiri and Kittler, Josef},
|
||||
booktitle = {DAGM},
|
||||
year = {2003}
|
||||
}
|
||||
@inproceedings{ChumEpipolar,
|
||||
author={Chum, Ondrej and Werner, Tomas and Matas, Jiri},
|
||||
booktitle={Proceedings of the 17th International Conference on Pattern Recognition. ICPR 2004},
|
||||
title={Epipolar geometry estimation via RANSAC benefits from the oriented epipolar constraint},
|
||||
year={2004},
|
||||
volume={1},
|
||||
pages={112-115 Vol.1}
|
||||
}
|
||||
@inproceedings{ChumDominant,
|
||||
title = {Epipolar Geometry Estimation Unaffected by the Dominant Plane},
|
||||
author = {Chum, Ondrej and Werner, Tomas and Matas, Jiri.},
|
||||
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year = {2005}
|
||||
}
|
||||
@article{FischlerRANSAC,
|
||||
author = {Fischler, Martin A. and Bolles, Robert C.},
|
||||
title = {Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography},
|
||||
year = {1981},
|
||||
publisher = {Association for Computing Machinery},
|
||||
volume = {24},
|
||||
number = {6},
|
||||
month = {jun},
|
||||
pages = {381–395},
|
||||
numpages = {15}
|
||||
}
|
||||
@article{Matas2005RandomizedRW,
|
||||
title={Randomized RANSAC with sequential probability ratio test},
|
||||
author={Matas, Jiri and Chum, Ondrej},
|
||||
journal={Tenth IEEE International Conference on Computer Vision (ICCV) Volume 1},
|
||||
year={2005},
|
||||
volume={2},
|
||||
pages={1727-1732 Vol. 2}
|
||||
}
|
||||
@inproceedings{MyattNAPSAC,
|
||||
author = {Myatt, D. and Torr, Philip and Nasuto, Slawomir and Bishop, John and Craddock, R.},
|
||||
year = {2002},
|
||||
booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
|
||||
title = {NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag}
|
||||
}
|
||||
@article{SteweniusRecent,
|
||||
author = {Stewenius, Henrik and Engels, Christopher and Nister, David},
|
||||
year = {2006},
|
||||
month = {06},
|
||||
pages = {284-294},
|
||||
title = {Recent developments on direct relative orientation},
|
||||
volume = {60},
|
||||
journal = {ISPRS Journal of Photogrammetry and Remote Sensing}
|
||||
}
|
||||
|
||||
@ -4,6 +4,7 @@ Interactive camera calibration application {#tutorial_interactive_calibration}
|
||||
@tableofcontents
|
||||
|
||||
@prev_tutorial{tutorial_real_time_pose}
|
||||
@next_tutorial{tutorial_usac}
|
||||
|
||||
| | |
|
||||
| -: | :- |
|
||||
|
||||
@ -6,3 +6,4 @@ Camera calibration and 3D reconstruction (calib3d module) {#tutorial_table_of_co
|
||||
- @subpage tutorial_camera_calibration
|
||||
- @subpage tutorial_real_time_pose
|
||||
- @subpage tutorial_interactive_calibration
|
||||
- @subpage tutorial_usac
|
||||
|
||||
@ -1,14 +1,19 @@
|
||||
---
|
||||
author:
|
||||
- Maksym Ivashechkin
|
||||
bibliography: 'bibs.bib'
|
||||
csl: 'acm-sigchi-proceedings.csl'
|
||||
date: August 2020
|
||||
title: 'Google Summer of Code: Improvement of Random Sample Consensus in OpenCV'
|
||||
...
|
||||
USAC: Improvement of Random Sample Consensus in OpenCV {#tutorial_usac}
|
||||
==============================
|
||||
|
||||
@tableofcontents
|
||||
|
||||
@prev_tutorial{tutorial_interactive_calibration}
|
||||
|
||||
| | |
|
||||
| -: | :- |
|
||||
| Original author | Maksym Ivashechkin |
|
||||
| Compatibility | OpenCV >= 4.0 |
|
||||
|
||||
This work was integrated as part of the Google Summer of Code (August 2020).
|
||||
|
||||
Contribution
|
||||
============
|
||||
------
|
||||
|
||||
The integrated part to OpenCV `calib3d` module is RANSAC-based universal
|
||||
framework USAC (`namespace usac`) written in C++. The framework includes
|
||||
@ -20,25 +25,25 @@ components:
|
||||
|
||||
1. Sampling method:
|
||||
|
||||
1. Uniform – standard RANSAC sampling proposed in \[8\] which draw
|
||||
1. Uniform – standard RANSAC sampling proposed in @cite FischlerRANSAC which draw
|
||||
minimal subset independently uniformly at random. *The default
|
||||
option in proposed framework*.
|
||||
|
||||
2. PROSAC – method \[4\] that assumes input data points sorted by
|
||||
2. PROSAC – method @cite ChumPROSAC that assumes input data points sorted by
|
||||
quality so sampling can start from the most promising points.
|
||||
Correspondences for this method can be sorted e.g., by ratio of
|
||||
descriptor distances of the best to second match obtained from
|
||||
SIFT detector. *This is method is recommended to use because it
|
||||
can find good model and terminate much earlier*.
|
||||
|
||||
3. NAPSAC – sampling method \[10\] which takes initial point
|
||||
3. NAPSAC – sampling method @cite MyattNAPSAC which takes initial point
|
||||
uniformly at random and the rest of points for minimal sample in
|
||||
the neighborhood of initial point. This is method can be
|
||||
potentially useful when models are localized. For example, for
|
||||
plane fitting. However, in practise struggles from degenerate
|
||||
issues and defining optimal neighborhood size.
|
||||
|
||||
4. Progressive-NAPSAC – sampler \[2\] which is similar to NAPSAC,
|
||||
4. Progressive-NAPSAC – sampler @cite barath2019progressive which is similar to NAPSAC,
|
||||
although it starts from local and gradually converges to
|
||||
global sampling. This method can be quite useful if local models
|
||||
are expected but distribution of data can be arbitrary. The
|
||||
@ -56,7 +61,7 @@ components:
|
||||
default option in framework*. The model might not have as many
|
||||
inliers as using RANSAC score, however will be more accurate.
|
||||
|
||||
3. MAGSAC – threshold-free method \[3\] to compute score. Using,
|
||||
3. MAGSAC – threshold-free method @cite BarathMAGSAC to compute score. Using,
|
||||
although, maximum sigma (standard deviation of noise) level to
|
||||
marginalize residual of point over sigma. Score of the point
|
||||
represents likelihood of point being inlier. *Recommended option
|
||||
@ -86,7 +91,7 @@ components:
|
||||
|
||||
4. Degeneracy:
|
||||
|
||||
1. DEGENSAC – method \[7\] which for Fundamental matrix estimation
|
||||
1. DEGENSAC – method @cite ChumDominant which for Fundamental matrix estimation
|
||||
efficiently verifies and recovers model which has at least 5
|
||||
points in minimal sample lying on the dominant plane.
|
||||
|
||||
@ -96,11 +101,11 @@ components:
|
||||
in minimal sample lie on the same side w.r.t. to any line
|
||||
crossing any two points in sample (does not assume reflection).
|
||||
|
||||
3. Oriented epipolar constraint – method \[6\] for epipolar
|
||||
3. Oriented epipolar constraint – method @cite ChumEpipolar for epipolar
|
||||
geometry which verifies model (fundamental and essential matrix)
|
||||
to have points visible in the front of the camera.
|
||||
|
||||
5. SPRT verification – method \[9\] which verifies model by its
|
||||
5. SPRT verification – method @cite Matas2005RandomizedRW which verifies model by its
|
||||
evaluation on randomly shuffled points using statistical properties
|
||||
given by probability of inlier, relative time for estimation,
|
||||
average number of output models etc. Significantly speeding up
|
||||
@ -109,17 +114,17 @@ components:
|
||||
|
||||
6. Local Optimization:
|
||||
|
||||
1. Locally Optimized RANSAC – method \[5\] that iteratively
|
||||
1. Locally Optimized RANSAC – method @cite ChumLORANSAC that iteratively
|
||||
improves so-far-the-best model by non-minimal estimation. *The
|
||||
default option in framework. This procedure is the fastest and
|
||||
not worse than others local optimization methods.*
|
||||
|
||||
2. Graph-Cut RANSAC – method \[1\] that refine so-far-the-best
|
||||
2. Graph-Cut RANSAC – method @cite BarathGCRANSAC that refine so-far-the-best
|
||||
model, however, it exploits spatial coherence of the
|
||||
data points. *This procedure is quite precise however
|
||||
computationally slower.*
|
||||
|
||||
3. Sigma Consensus – method \[3\] which improves model by applying
|
||||
3. Sigma Consensus – method @cite BarathMAGSAC which improves model by applying
|
||||
non-minimal weighted estimation, where weights are computed with
|
||||
the same logic as in MAGSAC score. This method is better to use
|
||||
together with MAGSAC score.
|
||||
@ -152,7 +157,7 @@ components:
|
||||
|
||||
4. Essential matrix – 4 null vectors are found using
|
||||
Gaussian elimination. Then the solver based on Gröbner basis
|
||||
described in \[11\] is used. Essential matrix can be computed
|
||||
described in @cite SteweniusRecent is used. Essential matrix can be computed
|
||||
only if <span style="font-variant:small-caps;">LAPACK</span> or
|
||||
<span style="font-variant:small-caps;">Eigen</span> are
|
||||
installed as it requires eigen decomposition with complex
|
||||
@ -180,12 +185,12 @@ sequentially. However, using default options of framework parallel
|
||||
RANSAC is not deterministic since it depends on how often each thread is
|
||||
running. The easiest way to make it deterministic is using PROSAC
|
||||
sampler without SPRT and Local Optimization and not for Fundamental
|
||||
matrix, because they internally use random generators.\
|
||||
\
|
||||
matrix, because they internally use random generators.
|
||||
|
||||
For NAPSAC, Progressive NAPSAC or Graph-Cut methods is required to build
|
||||
a neighborhood graph. In framework there are 3 options to do it:
|
||||
|
||||
1. `NEIGH_FLANN_KNN` – estimate neighborhood graph using OpenCV FLANN
|
||||
1. NEIGH_FLANN_KNN – estimate neighborhood graph using OpenCV FLANN
|
||||
K nearest-neighbors. The default value for KNN is 7. KNN method may
|
||||
work good for sampling but not good for GC-RANSAC.
|
||||
|
||||
@ -193,14 +198,14 @@ a neighborhood graph. In framework there are 3 options to do it:
|
||||
points which distance is less than 20 pixels.
|
||||
|
||||
3. `NEIGH_GRID` – for finding points’ neighborhood tiles points in
|
||||
cells using hash-table. The method is described in \[2\]. Less
|
||||
cells using hash-table. The method is described in @cite barath2019progressive. Less
|
||||
accurate than `NEIGH_FLANN_RADIUS`, although significantly faster.
|
||||
|
||||
Note, `NEIGH_FLANN_RADIUS` and `NEIGH_FLANN_RADIUS` are not able to PnP
|
||||
solver, since there are 3D object points.\
|
||||
\
|
||||
New flags:
|
||||
solver, since there are 3D object points.
|
||||
|
||||
New flags:
|
||||
------
|
||||
1. `USAC_DEFAULT` – has standard LO-RANSAC.
|
||||
|
||||
2. `USAC_PARALLEL` – has LO-RANSAC and RANSACs run in parallel.
|
||||
@ -220,9 +225,10 @@ New flags:
|
||||
|
||||
Every flag uses SPRT verification. And in the end the final
|
||||
so-far-the-best model is polished by non minimal estimation of all found
|
||||
inliers.\
|
||||
\
|
||||
inliers.
|
||||
|
||||
A few other important parameters:
|
||||
------
|
||||
|
||||
1. `randomGeneratorState` – since every USAC solver is deterministic in
|
||||
OpenCV (i.e., for the same points and parameters returns the
|
||||
@ -240,6 +246,7 @@ A few other important parameters:
|
||||
estimation on low number of points is faster and more robust.
|
||||
|
||||
Samples:
|
||||
------
|
||||
|
||||
There are three new sample files in opencv/samples directory.
|
||||
|
||||
@ -260,48 +267,3 @@ There are three new sample files in opencv/samples directory.
|
||||
3. `essential_mat_reconstr.py` – the same functionality as in .cpp
|
||||
file, however instead of clustering points to plane the 3D map of
|
||||
object points is plot.
|
||||
|
||||
References:
|
||||
|
||||
1\. Daniel Barath and Jiří Matas. 2018. Graph-Cut RANSAC. In *Proceedings
|
||||
of the iEEE conference on computer vision and pattern recognition*,
|
||||
6733–6741.
|
||||
|
||||
2\. Daniel Barath, Maksym Ivashechkin, and Jiri Matas. 2019. Progressive
|
||||
NAPSAC: Sampling from gradually growing neighborhoods. *arXiv preprint
|
||||
arXiv:1906.02295*.
|
||||
|
||||
3\. Daniel Barath, Jana Noskova, Maksym Ivashechkin, and Jiri Matas.
|
||||
2020. MAGSAC++, a fast, reliable and accurate robust estimator. In
|
||||
*Proceedings of the iEEE/CVF conference on computer vision and pattern
|
||||
recognition (cVPR)*.
|
||||
|
||||
4\. O. Chum and J. Matas. 2005. Matching with PROSAC-progressive sample
|
||||
consensus. In *Computer vision and pattern recognition*.
|
||||
|
||||
5\. O. Chum, J. Matas, and J. Kittler. 2003. Locally optimized RANSAC. In
|
||||
*Joint pattern recognition symposium*.
|
||||
|
||||
6\. O. Chum, T. Werner, and J. Matas. 2004. Epipolar geometry estimation
|
||||
via RANSAC benefits from the oriented epipolar constraint. In
|
||||
*International conference on pattern recognition*.
|
||||
|
||||
7\. Ondrej Chum, Tomas Werner, and Jiri Matas. 2005. Two-view geometry
|
||||
estimation unaffected by a dominant plane. In *2005 iEEE computer
|
||||
society conference on computer vision and pattern recognition
|
||||
(cVPR’05)*, 772–779.
|
||||
|
||||
8\. M. A. Fischler and R. C. Bolles. 1981. Random sample consensus: A
|
||||
paradigm for model fitting with applications to image analysis and
|
||||
automated cartography. *Communications of the ACM*.
|
||||
|
||||
9\. Jiri Matas and Ondrej Chum. 2005. Randomized RANSAC with sequential
|
||||
probability ratio test. In *Tenth iEEE international conference on
|
||||
computer vision (iCCV’05) volume 1*, 1727–1732.
|
||||
|
||||
10\. D. R. Myatt, P. H. S. Torr, S. J. Nasuto, J. M. Bishop, and R.
|
||||
Craddock. 2002. NAPSAC: High noise, high dimensional robust estimation.
|
||||
In *In bMVC02*, 458–467.
|
||||
|
||||
11\. Henrik Stewénius, Christopher Engels, and David Nistér. 2006. Recent
|
||||
developments on direct relative orientation.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user