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Main purpose of this is to bring new gflags library which is more likely
to have a fix for undefined order of static variables initialization and
also to bring new glog where some compilation error are fixed (which are
only visible with more strict checks with clang and c++11 enabled).
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This starts the creating the new AutoTrack API. The new API will
make it possible for libmv to do full autotracking, including
predictive tracking and also support multiple motion models (3D
planes etc).
The first goal (not in this patch) is to convert Blender to use
the new API without adding any new functionality.
This API currently contanins:
- Frame accessor to access frames which are stored in Blender side.
- New Tracks implementation
- New Reconstruction implementation
Currently this API only tested on doing the same frame-to-frame
tracking as the old API allowed to do. But it also supports now
predictive tracking which is based on the Kalman filter.
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Currently covers only small set of functionality.
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It was rather stupid having it in brute region tracker,
now it is in own file in base library.
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This commit makes it so CameraIntrinsics is no longer hardcoded
to use the traditional polynomial radial distortion model. Currently
the distortion code has generic logic which is shared between
different distortion models, but had no other models until now.
This moves everything specific to the polynomial radial distortion
to a subclass PolynomialDistortionCameraIntrinsics(), and adds a
new division distortion model suitable for cameras such as the
GoPro which have much stronger distortion due to their fisheye lens.
This also cleans up the internal API of CameraIntrinsics to make
it easier to understand and reduces old C-style code.
New distortion model is available in the Lens panel of MCE.
- Polynomial is the old well-known model
- Division is the new one which s intended to deal better with huge
distortion.
Coefficients of this model works independent from each other
and for division model one probably want to have positive values
to have a barrel distortion.
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FAST detector has been replaced with fancier Harris,
so no need to keep FAST library in the sources now.
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Switch the detector API to a single function which accepts
a float image and detector options. This makes usage of
feature detection more unified across different algorithms.
Options structure is pretty much straightforward and contains
detector to be used and all the detector-specific settings.
Also implemented Harris feature detection algorithm which
is not as fast as FAST one but is expected to detect more
robust feature points. It is also likely that less features
are detected, but better quality than quantity.
Blender will now use Harris detector by default, later we'll
remove FAST detector.
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They were giving compilation error with msvc2012,
and instead of having workaround in them let's
drop them away form blender (they're not used even).
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Implements an automatic keyframe selection algorithm which uses
couple of approaches to find out best keyframes candidates:
- First, slightly modifier Pollefeys's criteria is used, which
limits correspondence ration from 80% to 100%. This allows to
reject keyframe candidate early without doing heavy math in
cases there're not much common features with first keyframe.
- Second step is based on Geometric Robust Information Criteria
(aka GRIC), which checks whether features motion between
candidate keyframes is better defined by homography or
fundamental matrices.
To be a good keyframe candidate, fundamental matrix need to
define motion better than homography (in this case F-GRIC will
be smaller than H-GRIC).
This two criteria are well described in this paper:
http://www.cs.ait.ac.th/~mdailey/papers/Tahir-KeyFrame.pdf
- Final step is based on estimating reconstruction error of
a full-scene solution using candidate keyframes. This part
is based on the following paper:
ftp://ftp.tnt.uni-hannover.de/pub/papers/2004/ECCV2004-TTHBAW.pdf
This step requires reconstruction using candidate keyframes
and obtaining covariance matrix of 3D points positions.
Reconstruction was done pretty much straightforward using
other simple pipeline routines, and for covariance estimation
pseudo-inverse of Hessian is used, which is in this case
(J^T * J)+, where + denotes pseudo-inverse.
Jacobian matrix is estimating using Ceres evaluate API.
This is also crucial to get rid of possible gauge ambiguity,
which is in our case made by zero-ing 7 (by gauge freedoms
number) eigen values in pseudo-inverse.
There're still room for improving and optimizing the code,
but we need some point to start with anyway :)
Thanks to Keir Mierle and Sameer Agarwal who assisted a lot
to make this feature working.
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Made it so reconstructed scene always scaled in a way
that variance of camera centers is unity.
This solves "issues" when different keyframes will
give the same reprojection error but will give scenes
with different.scale, which could easily have been
considered as a bad keyframe combination.
This change is essential for automatic keyframe
selection algorithm to work reliable for user.
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- Solves some strict compilation warning
- Style/code cleanup
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There're some features planned which would
require rigid registration, but this code
would need to be re-done anyway to use new
minimizer and solving some issues with ICP
algorithm there.
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Several major things are done in this commit:
- First of all, logic of modal solver was changed.
We do not rely on only minimizer to take care of
guessing rotation for frame, but we're using
analytical rotation computation for point clouds
to obtain initial rotation.
Then this rotation is being refined using Ceres
minimizer and now instead of minimizing average
distance between points of point of two clouds,
minimization of reprojection error of point
cloud onto frame happens.
This gives quite a bit of precision improvement.
- Second bigger improvement here is using bundle
adjustment for a result of first step when we're
only estimating rotation between neighbor images
and reprojecting markers.
This averages error across the image sequence
avoiding error accumulation. Also, this will
tweak bundles themselves a bit for better match.
- And last bigger improvement here is support of
camera intrinsics refirenment.
This allowed to significantly improve solution
for real-life footage and results after such
refining are much more usable than it were before.
Thanks to Keir for the help and code review.
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Should be no functional changes.
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Added modal solver module which is needed for tripod solving.
For details of this solver read changelog of libmv.
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implementation
Currently not used in blender code but is needed for some current work.
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compilation of glog on hurd platform.
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This version of libmv includes new gflags and glog libraries which makes
it possible to compile libmv with clang compiler.
Also remove code from CMakeLists which was disabling libmv if using clang.
Tested on linux with gcc-4.6 and clang-3.0, windows cmake+msvc and scons+mingw.
Could be some issues with other platforms/build system which shall be simple to resolve.
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Also updated generation scripts and templates
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===========================
Commiting camera tracking integration gsoc project into trunk.
This commit includes:
- Bundled version of libmv library (with some changes against official repo,
re-sync with libmv repo a bit later)
- New datatype ID called MovieClip which is optimized to work with movie
clips (both of movie files and image sequences) and doing camera/motion
tracking operations.
- New editor called Clip Editor which is currently used for motion/tracking
stuff only, but which can be easily extended to work with masks too.
This editor supports:
* Loading movie files/image sequences
* Build proxies with different size for loaded movie clip, also supports
building undistorted proxies to increase speed of playback in
undistorted mode.
* Manual lens distortion mode calibration using grid and grease pencil
* Supervised 2D tracking using two different algorithms KLT and SAD.
* Basic algorithm for feature detection
* Camera motion solving. scene orientation
- New constraints to "link" scene objects with solved motions from clip:
* Follow Track (make object follow 2D motion of track with given name
or parent object to reconstructed 3D position of track)
* Camera Solver to make camera moving in the same way as reconstructed camera
This commit NOT includes changes from tomato branch:
- New nodes (they'll be commited as separated patch)
- Automatic image offset guessing for image input node and image editor
(need to do more tests and gather more feedback)
- Code cleanup in libmv-capi. It's not so critical cleanup, just increasing
readability and understanadability of code. Better to make this chaneg when
Keir will finish his current patch.
More details about this project can be found on this page:
http://wiki.blender.org/index.php/User:Nazg-gul/GSoC-2011
Further development of small features would be done in trunk, bigger/experimental
features would first be implemented in tomato branch.
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