Mainly fixed some style warnings reported by cpplint.
Also changed how camera (un)distortion happens internally
by replacing number of channels as a template argument
with number as channels passing as function argument.
Makes code easier to follow by eliminating loads checks
how much channels are used and which argument to pass to
the template.
Summary:
This brings up much easier termination type usage,
which for us means we might use:
ceres::Summary::IsSolutionUsable()
instead of doing manual funky enum values check.
Reviewers: keir
Differential Revision: https://developer.blender.org/D153
Changes for VC2013
Now, I can build Blender with VC2013 with Cycles, Collada, OpenExr,OpenImageIO disabled. Also, you need VC2008 sp1 installed to make old libs compatible.
The issue was caused by the fact that in this version
of MSVC unordered_map class is defined in <unordered_map>
header file, but this file declares the class int std::tr1
namespace.
This confused existing assumption that if there's an
existing <unordered_map> file then class is declared
in std namespace.
Added an extra check to CMake which detects whether
it's std or std::tr1 which actually contains class
of unordered_map.
This might be changed/cleaned in the future, for
now committing to our repository to solve compilation
error on windows.
Details of the patch in upstream can be found there:
https://ceres-solver-review.googlesource.com/#/c/4371/
- A richer Problem API.
- DynamicNumericDiffCostFunction.
- Faster ITERATIVE_SCHUR solver.
- Faster SCHUR_JACOBI preconditioner.
- Faster Jacobian evaluation.
- Faster visibility based preconditioning using single linkage clustering.
Also re-wrote rules for unordered collections detection,
should work on all platforms and compilers now :)
- random.hpp was only removed from actual include
directory, but not from patches/files.
- Files list generator didn't ignore config.h file
which in fact is not needed.
- A bit more reasonable name for the estimation option
structure and estimation functions.
- Get rid of unclear function and parameter tolerance,
it wasn't clear at all in which units they are.
Now we've got expected_average_symmetric_distance as
an early output check and as soon as average symmetric
error goes below this threshold refining finishes.
This distance is measured in the same units as input
points are.
It is arguable whether we need callback for this or not,
but seems Ceres doesn't have some kind of absolute
threshold for function value and function_tolerance
behaves different from logic behind expected symmetric
error.
- Added option to normalize correspondences before
estimating the homography in order to increase estimation
stability. See
R. Hartley and A. Zisserman. Multiple View Geometry in Computer
Vision. Cambridge University Press, second edition, 2003.
https://www.cs.ubc.ca/grads/resources/thesis/May09/Dubrofsky_Elan.pdf
Before the refinement phase of tracking, a brute force SAD search
is run across the search area. This works well but is slow;
especially if the guess for the track's location is accurate.
This patch runs a refinement phase before running a brute force
search, hoping that the guessed position (in x2, y2) is close to
the best answer. If it is, then no brute search is done. If it is
not, then a normal brute force search followed by refinement is
done.
In some cases this may produce worse tracks than before; the
regressions will need investigation. The predictive motion model
(to be implemented) will reduce the probability of that happening.
Before this patch, if Ceres returned USER_SUCCESS indicating that
Ceres was only changing the tracked quad slightly between
iterations (indicating convergence), no final correlation check
was done. This leads to incorrectly returning that the tracking
was successful, when it actually failed.
Added a weight slider to track which defines
how much particular track affects in a final
reconstruction. This weight is for sure
animateable.
Currently it affects on BA step only which in
most cases will work just fine.
The usecase of this slider is to have it set
to 1.0 most of the time where the track is
good, but blend it's weight down to 0 when
tracker looses the track. This will prevent
camera from jump.
Tutorial is to be done by Sebastian.
It was rather confusing from the user usage point
of view and didn't get so much improvement after
new bundle adjuster was added.
In the future we might want to switch resection
to PPnP algorithm, which could also might be a
nice alternative to fallback option.
- Tweaked typedefs in stdint so they match
what we've got in BLI_sys_types (needed to
explicitly tell sign to MSVC).
Not so much harmful to be more explicit here,
but we really better to have single stdint
int blender.
- Tweaked allocations macros so MSVC is happy
with structures allocation.
Also made libmv-capi use guarded objetc allocation.
Run into some suspecious cases when it was not so
clear whether memory is being freed or not.
Now we'll know for sure whether there're leaks or not :)
Having this macros in a guardedalloc header helps
using them in other areas (for now it's OCIO and libmv,
but in the future it'll be more places).
Jittering was caused by homography not being estimated
accurate enough.
Before this, only algebraic estimation was used, which
is indeed not so much great, Now use algebraic estimation
followed with refinement step using Ceres minimizer.
The code was already there since keyframe selection patch,
made such estimation a generic function in multiview/ and
changed API for estimation in order to pass all additional
options via an options structure (the same way as it's
done fr Ceres).
This includes changes to both homography and fundamental
estimation.
TODO:
- Need to document Ceres functors better.
- Need to support homogeneous coordinates (currently
only euclidean coords are supported).
For Blender this release is interesting because of:
- Covariance estimation (not used in Blender yet, but now we
might use it for keyframe selection instead of havingown
implementation).
- Significant performance improvements to loss function and
dense linear solvers and automatic differentiation.
Unfortunately, didn't notice speedup of tracking itself,
but camera reconstruction now happens around 2 times faster
on my laptop,
- Better inner iteration step acceptance and stopping.
Particle system code used global variable to sort hair by orig index,
which is not safe for threading at all.
Replaced this with usage of reentrant version of qsort, which is
now implemented in BLI. It was moved from recast navigation code
to BLI, so more areas could use it (if needed).
--
svn merge -r59086:59087 ^/branches/soc-2013-depsgraph_mt
This commit includes all the changes made for plane tracker
in tomato branch.
Movie clip editor changes:
- Artist might create a plane track out of multiple point
tracks which belongs to the same track (minimum amount of
point tracks is 4, maximum is not actually limited).
When new plane track is added, it's getting "tracked"
across all point tracks, which makes it stick to the same
plane point tracks belong to.
- After plane track was added, it need to be manually adjusted
in a way it covers feature one might to mask/replace.
General transform tools (G, R, S) or sliding corners with
a mouse could be sued for this. Plane corner which
corresponds to left bottom image corner has got X/Y axis
on it (red is for X axis, green for Y).
- Re-adjusting plane corners makes plane to be "re-tracked"
for the frames sequence between current frame and next
and previous keyframes.
- Kayframes might be removed from the plane, using Shit-X
(Marker Delete) operator. However, currently manual
re-adjustment or "re-track" trigger is needed.
Compositor changes:
- Added new node called Plane Track Deform.
- User selects which plane track to use (for this he need
to select movie clip datablock, object and track names).
- Node gets an image input, which need to be warped into
the plane.
- Node outputs:
* Input image warped into the plane.
* Plane, rasterized to a mask.
Masking changes:
- Mask points might be parented to a plane track, which
makes this point deforming in a way as if it belongs
to the tracked plane.
Some video tutorials are available:
- Coder video: http://www.youtube.com/watch?v=vISEwqNHqe4
- Artist video: https://vimeo.com/71727578
This is mine and Keir's holiday code project :)
This adds a new Euclidean resection method, used to create the
initial reconstruction in the motion tracker, to libmv. The method
is based on the Procrustes PNP algorithm (aka "PPnP"). Currently
the algorithm is not connected with the motion tracker, but it
will be eventually since it supports initialization.
Having an initial guess when doing resection is important for
ambiguous cases where potentially the user could offer extra
guidance to the solver, in the form of "this point is in front of
that point".
--
svn merge -r58821:58822 ^/branches/soc-2011-tomato
Clean up inconsistencies in the libmv C API:
- All type identifiers are libmv_TypeName
- All function identifiers libmv_functionName
- Prefer libmv_nounVerb function names (e.g. libmv_featuresDestroy)
- Match Blender code formatting rather than Google
- Spelling corrections
Code review: https://codereview.appspot.com/11494044/
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.
