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)
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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.
Fix invalid memory access in USAC #27865
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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](https://github.com/terzakig/sqpnp/commit/a03d34b641ebba2986cf457cd910218cc8d3cc8c) 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.
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Specify DLS and UPnP mappings to EPnP in all places for solvePnP* tests #27185
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FAN YUCHEN
Pierre Chatelier
Stephan T. Lavavej
Sofian Elmotiem
Alexander Smorkalov
Kumataro
Vincent Rabaud
Alexander Smorkalov
Rohan Mistry
Souriya Trinh
Murat Raimbekov
hyndhavamahesh345
HAILOM ASEGEDE
AdwaithBatchu
Adrian Kretz
Ismail Abou Zeid
Vladyslav Humennyy
MaximSmolskiy
Manolis Lourakis
Dmitry Kurtaev
omahs
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