calib3d: fix division by zero in SGBM 3-way mode when uniquenessRatio is 100 - #29762Fixes#29761.
`SGBM3WayMainLoop` derives its uniqueness threshold in the SIMD path as `(100*min_cost)/(100-uniquenessRatio)`, so a `uniquenessRatio` of 100 divides by zero and the process dies with SIGFPE. The scalar fallback right below it, and the other SGBM modes, express the same test as `cost*(100 - uniquenessRatio) < min_cost*100`, which needs no division and copes with the value fine. `MODE_HH4` goes through `CalcHorizontalSums`, which never divides, so only `MODE_SGBM_3WAY` reproduces.
The SIMD shortcut is now skipped once `uniquenessRatio` reaches 100 and the scalar loop decides on its own, which is exactly what a build without SIMD already does. Ratios below 100 keep the fast path and produce identical output. The diff looks long because the existing block is indented one level, `?w=1` shows the real change.
The second commit fixes the neighbouring case: `thresh` grows to `100*min_cost` as the ratio approaches 100, well past `SHRT_MAX`, and `(short)(thresh+1)` wraps. On the reporter's image pair at ratio 99, 3-way marked 48723 pixels valid while `MODE_SGBM`, `MODE_HH` and `MODE_HH4` all landed near 48370; saturating brings it to 48371.
Verified with opencv_extra test data: `Calib3d_StereoSGBM.regression`, `Calib3d_StereoSGBM.deterministic`, `Calib3d_StereoSGBM_HH4.regression` and `Calib3d_StereoBM.regression` still pass. The new `Calib3d_StereoSGBM.regression_29761` aborts on unpatched 4.x under `-fsanitize=integer-divide-by-zero` and passes with the fix.
The same code sits at `modules/stereo/src/stereosgbm.cpp` on 5.x, which is the path the reporter cited. The module move means the merge will not apply cleanly, so tell me if you would rather have a separate 5.x PR.
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CV_Assert permits rvec/tvec as Size(1,3) or Size(3,1), but the
implementation only handled column vectors:
- LM path read rvec/tvec via .at<double>(i,0), OOB for a row vector.
- LM path's convertTo() write-back reallocated a local Mat alias
instead of writing in place whenever the source/dest shapes
mismatched, silently discarding the refined result for row vectors.
- VVS path's "R1 * tvec" requires a column vector; a row vector threw
a cv::Exception from gemm's shape assertion.
Fixed all three with shape-agnostic .at<double>(i) indexing and
reshape() before convertTo()/matrix arithmetic so orientation always
matches. Added Calib3d_SolvePnP.refine_row_vector covering both
solvePnPRefineLM and solvePnPRefineVVS with both orientations.
Fixes#29747
Fix calibration tutorial docs, decomposeProjectionMatrix, and convertMaps performance claims #29487Fixes#25655, #26791, #27277.
Three doc fixes:
1. Calibration tutorial: rows/cols swapped, fixed np.mgrid consistency
2. decomposeProjectionMatrix: clarified transVect is camera center in homogeneous coordinates
3. convertMaps: replaced overstated 2x speed claim
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More use of AutoBuffer #28907
When possible, AutoBuffer should be faster than std::vector<>, and should not be worse if it requires a heap allocation rather than a stack allocation.
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The relative neighborhood graph (RNG) is a subgraph of the Delaunay
triangulation, so only Delaunay edges are candidates for RNG membership.
The previous implementation tested all N^2 pairs against all N points,
giving O(N^3). The new implementation builds the Delaunay triangulation
with Subdiv2D (O(N log N)), then checks only those ~3N edges for the RNG
lune-emptiness condition, reducing computeRNG to O(N^2) in the worst case
and much better in practice for regular grids.
Added synthetic-grid accuracy tests for both symmetric and asymmetric
patterns across several sizes, and a perf test parameterized by grid size.
doc: modernize Doxygen comments to support for v1.15.0 #28903
This PR addresses several documentation build failures encountered with modern Doxygen versions, particularly v1.15.0 (shipped with Ubuntu 26.04).
- flann module: Updated license headers from /**** to /*M****. This prevents Doxygen from misinterpreting the license text (specifically the unclosed backticks in ``AS IS'') as documentation blocks, which previously caused "Reached end of file" errors.
- core module: Fixed a typo in operations.hpp where a doubled backtick (``) caused parsing to fail.
- tutorials: Fixed a missing backtick in real_time_pose.markdown (around line 108) and corrected typos in the RobustMatcher class name.
- Links: Resolved explicit link request failures in calib3d.hpp by ensuring proper namespace resolution.
These fixes ensure that the documentation can be generated without errors on the latest toolchains.
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Bump some calib3d files to C++ #28825
This is just copy/pasting files from 5.x (first commit) and applying some light patch for compilation (second commit).
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Optimize calibrateCamera with Schur‑complement LM and parallel Jacobian accumulation #28461
## Summary
- Optimized `calibrateCamera` for faster runtime without changing outputs using Schur‑complement LM, Parallel Jacobian accumulation, alongside other optimizations.
- Reduced time complexity from O(n^3) to O(n)
- Add a perf test that uses a 500-image chessboard dataset for performance testing.
## Performance
<img width="1200" height="800" alt="base_vs_fast_results" src="https://github.com/user-attachments/assets/6dafa19f-f9cb-4f7f-ba40-0940373712e8" />
<img width="1200" height="800" alt="fast_vs_ceres_results" src="https://github.com/user-attachments/assets/7157af27-8a2b-4810-8b53-3cc9972a8493" />
<img width="1200" height="800" alt="base_vs_fast_param_deviation" src="https://github.com/user-attachments/assets/fe4f954c-34f9-4b9a-b1b2-46e4c76ce08c" />
[Testing repo
](https://github.com/Ron12777/OpenCV-benchmarking)
## Testing
- All local tests pass
## Related
- [opencv_extra PR with test images](https://github.com/opencv/opencv_extra/pull/1312)
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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docs(calib3d): clarify coordinate systems in projectPoints, undistort Points, and fisheye::undistortPoints #28333
This PR improves documentation clarity by explicitly describing:
- Input/output coordinate systems (pixel vs normalized coordinates)
- When to use normalized vs pixel coordinates in undistortPoints output
- Differences between fisheye and standard pinhole camera models
- Coordinate system transformations for better understanding
Changes:
- projectPoints: Added explicit note about input (world coords) and output (pixel coords)
- undistortPoints: Clarified that input is pixel coordinates, and output depends on parameter P (normalized vs pixel)
- fisheye::undistortPoints: Added comprehensive documentation including coordinate systems, when to use fisheye vs standard model, and distortion coefficient differences
These improvements help users avoid ambiguity when using these functions for camera calibration and 3D vision pipelines.
docs: fix spelling errors in documentation and code #28301
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### Description
Fixed multiple spelling errors across documentation, comments, and code:
- 'colummn' → 'column' (cublas.hpp, 3 occurrences)
- 'points_per_colum' → 'points_per_column' (calib3d.hpp, 3 occurrences)
- 'Asignee' → 'Assignee' (sift files, 2 occurrences)
- 'compability' → 'compatibility' (face.hpp, 2 occurrences)
- 'orignal' → 'original' (aruco_detector.cpp)
- 'refrence' → 'reference' (chessboard.cpp)
- 'indeces' → 'indices' (stitching.hpp)
- 'OutputPrecison' → 'OutputPrecision' (test)
- 'tranform' → 'transform' (slice_layer.cpp, 3 occurrences)
Total: 24 fixes across 14 files. Documentation and comment changes only, no functional impact.
Prevent a potential crash in FMEstimatorCallback::runKernel #28361
With solveCubic sometimes failing, n can be -1.
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core: suppress LAPACK deprecation warnings on macOS #28203
### Description
On macOS (Apple Silicon) with newer Xcode/Clang, the CLAPACK interface is deprecated.
Compiling `hal_internal.cpp` triggers multiple warnings like:
`warning: 'sgesv_' is deprecated: first deprecated in macOS 13.3 - The CLAPACK interface is deprecated. [-Wdeprecated-declarations]`
Since migrating to the new Accelerate interface (Apple's recommended fix) requires significant changes to the HAL implementation and breaks the build if headers are mismatched, this PR takes the pragmatic approach. It suppresses the `-Wdeprecated-declarations` warning for this specific file using Clang pragmas to clean up the build output.
### Verification
- [x] Verified build is silent on macOS (M3).
- [x] Verified pragmas are correctly scoped within `HAVE_LAPACK` to prevent scope mismatch on non-LAPACK builds.
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Fixed issues identified by PVS Studio #28185
Partially fixes https://github.com/opencv/opencv/issues/28167
Paper: https://pvs-studio.com/en/blog/posts/cpp/1321/
Closed items: N2, N4, N5, N6, N7, N8, N10, N11, N13, N14.
To be continued...
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Undistort points convergence #27993
I have looked into the `undistortPoints()` problem of issue #27916 and have found a solution. The problem is, as @Linhuihang has correctly pointed out, that the fixed-point iterations do not converge. Here are the functions which are optimized for the undistortion problem:
$$
\begin{aligned}
r^2 &= x'^2 + y'^2 \\
f_1(x') &= \frac{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}{1 + k_1 r^2 + k_2 r^4 + k_3 r^6} (x'' - 2p_1 x' y' - p_2(r^2 + 2 x'^2) - s_1 r^2 + s_2 r^4) = x' \\
f_2(y') &= \frac{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}{1 + k_1 r^2 + k_2 r^4 + k_3 r^6} (y'' - p_1 (r^2 + 2 y'^2) - 2 p_2 x' y' - s_3 r^2 - s_4 r^4) = y'
\end{aligned}
$$
where $x', y'$ are the undistorted points we want to compute and and $x'', y''$ are the given distorted points. This problem is solved using fixed-point iterations like
$$
x'_{k+1} = f_1(x'_k),\quad
y'_{k+1} = f_2(y'_k)
$$
I guess the issue here is that the distortion function does not necessarily satisfy the [Banach fixed-point theorem](https://en.wikipedia.org/wiki/Banach_fixed-point_theorem), i.e. the slope of the function can be too large. This can be seen in @Linhuihang's comment https://github.com/opencv/opencv/issues/27916#issuecomment-3417883642 - the point series jumps around and doesn't converge.
A common solution is to instead do damped fixed-point iterations, so that the updates are "more smooth".
$$
x'_{k+1} = (1 - \alpha) x'_k + \alpha f_1(x'_k),\quad
y'_{k+1} = (1 - \alpha) y'_k + \alpha f_2(y'_k)
$$
I have implemented a simple logic which starts with $\alpha = 1$ (so just like it is now) and reduces $\alpha$ whenever the optimization error would increase. This seems reasonable to me: the initial logic is to do normal fixed-point iterations and to gradually become "more damped" when we notice that we don't converge. Perhaps there is a better way to ensure convergence, but this is the most straightforward modification to the current code that I have found.
This problem is not due to the $\tau_x, \tau_y$ parameters; it also occurs when they are zero. In fact, the fixed-point iterations are done when the tilt correction of $\tau_x, \tau_y$ has already been applied. I have added a test to reproduce the problem. This PR fixes#27916.
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[calib3d] Add estimateTranslation2D() #27950
Merge with opencv_extra PR: opencv/opencv_extra#1286
### **Description**
This PR adds a new API, `cv::estimateTranslation2D()`, to the **calib3d** module.
It computes a **pure 2D translation** between two sets of corresponding points using robust methods (`RANSAC` and `LMedS`).
The function mirrors the interface and behavior of `estimateAffine2D()` and `estimateAffinePartial2D()`, but constrains the transformation to translation only.
This model is particularly useful for cases where the motion between images is purely translational, such as:
- Aerial stitching and planar mosaics.
- Image alignment in fixed-camera systems.
- Lightweight pipelines where affine or homography models are unnecessarily complex.
The implementation introduces a new internal class `Translation2DEstimatorCallback` and integrates seamlessly into OpenCV’s existing robust estimation framework (`PointSetRegistrator`).
---
### **Key Features**
- Implements `cv::estimateTranslation2D()` in the `calib3d` module.
- Supports robust methods **RANSAC** and **LMedS**.
- Adds accuracy and performance tests.
- Provides full **C++ and Python bindings**.
- Includes **Doxygen documentation** consistent with OpenCV’s standards.
- Verified correctness across noise, outlier, and datatype variations.
---
### **Testing & Verification**
**Unit Tests** (`modules/calib3d/`)
- **Minimal sample:**
`test1Point` validates that a single correspondence recovers the correct translation under both **RANSAC** and **LMedS** across 500 randomized trials.
- **Robustness to noise and outliers:**
`testNPoints` generates 100 correspondences, injects noise and outliers (≤40% for RANSAC, ≤50% for LMedS), and verifies that:
- Estimated **T** closely matches ground truth (`cvtest::norm(..., NORM_L2)`).
- Inlier mask consistency and correctness are maintained.
- **Datatype conversion:**
`testConversion` checks mixed input datatypes (integer → float) to ensure correct conversion and consistent results.
- **Input immutability:**
`dont_change_inputs` confirms that input arrays remain unchanged after function execution, mirroring affine behavior.
**Performance Tests** (`modules/calib3d/`)
- `EstimateTranslation2DPerf` benchmarks **RANSAC** and **LMedS** using:
- Point counts: 1000
- Confidence levels: 0.95
- Refinement iterations: 10, 0
These tests confirm **numerical stability**, **performance scaling**, and **consistency** across datatypes and noise levels.
---
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In `fisheye::initUndistortRectifyMap` states, that the function 'compensate radial and tangential lens distortion'. But in fact, fisheye camera model in OpenCV does not uses tangential distortion. It uses only radial distortions, with 4 distortion k_1, k_2, k_3, k_4, which all are radial. In the code of the function all of those koeficients indeed are used as radial lens distortion coefficients. Possible reason of that issue is similar documentation of pinhole camera, that as first four coefficients uses 2 radial and 2 tangential lens distortion coefficients - k_1, k_2, p_1, p_2.
Update Gao P3P with Ding P3P #27736
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---
The current Gao P3P implementation does not cover all the degenerate cases, **see last line** in: 6d889ee74c/modules/calib3d/src/p3p.cpp (L211-L221)
See also:
- https://github.com/opencv/opencv/issues/4854
---
<details>
<summary>OBSOLETE</summary>
To fix this, the USAC P3P from OpenCV 5 is used instead: 7e6da007cd/modules/3d/src/usac/pnp_solver.cpp (L282)
---
## Some results
### Old P3P vs new
In the following video, I have tried to highlight the viewpoints which cause issues:
https://github.com/user-attachments/assets/97bec6a6-4043-4509-b50e-a9856d6423bd
| | Old P3P | New P3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.045701 | 0.024816 |
| Median (ms) | 0.025146 | 0.023193 |
| Std (ms) | 0.028953 | 0.006124 |
### New P3P vs AP3P
https://github.com/user-attachments/assets/eaeb21dc-3ffd-4b6c-9902-4352f824aa45
The AP3 method is superior both in term of accuracy and computation time:
| | New P3P | AP3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.043750 | 0.023442 |
| Median (ms) | 0.023193 | 0.021484 |
| Std (ms) | 0.039920 | 0.005265 |
### New P3P vs AP3P (range test)
https://github.com/user-attachments/assets/572e7b7a-2966-4bed-8e0c-b93d863987dc
The implemented P3P method does not work well when the tag is small, at long range.
| | New P3P | AP3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.031351 | 0.025189 |
| Median (ms) | 0.022217 | 0.020996 |
| Std (ms) | 0.024920 | 0.009633 |
---
- I have tried to simplify the P3P code, hope I did not break the implementation code
- calculations are performed using double type for simplicity.
- code such as the following are redundant and no more needed and should be replaced by `cv::Rodrigues`:
6d889ee74c/modules/calib3d/src/usac/pnp_solver.cpp (L395)
</details>
Fix invalid memory access in USAC #27865
### Pull Request Readiness Checklist
Fix#27863
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Moved pattern generator to apps and rewrote tutorial #27833
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Fixed bugs in orthogonalization; simplified column vectors copying #27437
This PR mirrors to OpenCV a bug fix addressed by commit [a03d34b](a03d34b641) in SQPnP
It also fixes bugs in the orthogonalization introduced during the porting to OpenCV and simplifies column vectors copying, eliminating double loops.
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Fix typos #27338
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Add image dimension check to avoid StereoSGBM non-determinism #27305
Addresses #25828
Users noticed that StereoSGBM would occasionally give non-deterministic results for `.compute(imgL, imgR)`.
I and others traced the cause to out-of-bounds access that was not being caught when the input images were not wide enough for the input block size and number of disparities to StereoSGBM. The specific math and logic can be found in the above issue's discussion.
This PR adds a CV_Check to make sure images are wider than 1/2 of the block size + the max disparity the algorithm will search.
The check was only added to the regular `compute` method for StereoSGBM and not to the other modes, as I did not observe the non-deterministic behavior with the other compute modes like HH.
In addition, this PR adds a test case to Calib3d to make sure the check is being thrown in the problem case and that the results are deterministic in the good case.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Specify DLS and UPnP mappings to EPnP in all places for solvePnP* tests #27185
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
