forked from bartvdbraak/blender
93 lines
3.3 KiB
Plaintext
93 lines
3.3 KiB
Plaintext
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Description
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This is an implementation of a sparse Levenberg-Marquardt optimization
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procedure and several bundle adjustment modules based on it. There are three
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versions of bundle adjustment:
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1) Pure metric adjustment. Camera poses have 6 dof and 3D points have 3 dof.
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2) Common, but adjustable intrinsic and distortion parameters. This is useful,
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if the set of images are taken with the same camera under constant zoom
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settings.
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3) Variable intrinsics and distortion parameters for each view. This addresses
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the "community photo collection" setting, where each image is captured with
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a different camera and/or with varying zoom setting.
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There are two demo applications in the Apps directory, bundle_common and
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bundle_varying, which correspond to item 2) and 3) above.
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The input data file for both applications is a text file with the following
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numerical values:
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First, the number of 3D points, views and 2D measurements:
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<M> <N> <K>
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Then, the values of the intrinsic matrix
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[ fx skew cx ]
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K = [ 0 fy cy ]
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[ 0 0 1 ],
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and the distortion parameters according to the convention of the Bouget
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toolbox:
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<fx> <skew> <cx> <fy> <cy> <k1> <k2> <p1> <p2>
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For the bundle_varying application this is given <N> times, one for each
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camera/view.
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Then the <M> 3D point positions are given:
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<point-id> <X> <Y> <Z>
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Note: the point-ids need not to be exactly from 0 to M-1, any (unique) ids
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will do.
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The camera poses are given subsequently:
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<view-id> <12 entries of the RT matrix>
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There is a lot of confusion how to specify the orientation of cameras. We use
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projection matrix notation, i.e. P = K [R|T], and a 3D point X in world
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coordinates is transformed into the camera coordinate system by XX=R*X+T.
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Finally, the <K> 2d image measurements (given in pixels) are provided:
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<view-id> <point-id> <x> <y> 1
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See the example in the Dataset folder.
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Performance
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This software is able to perform successful loop closing for a video sequence
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containing 1745 views, 37920 3D points and 627228 image measurements in about
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16min on a 2.2 GHz Core 2. The footprint in memory was <700MB.
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Requirements
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Solving the augmented normal equation in the LM optimizer is done with LDL, a
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Cholsky like decomposition method for sparse matrices (see
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http://www.cise.ufl.edu/research/sparse/ldl). The appropriate column
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reordering is done with COLAMD (see
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http://www.cise.ufl.edu/research/sparse/colamd). Both packages are licensed
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under the GNU LGPL.
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This software was developed under Linux, but should compile equally well on
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other operating systems.
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-Christopher Zach (cmzach@cs.unc.edu)
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/*
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Copyright (c) 2008 University of North Carolina at Chapel Hill
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This file is part of SSBA (Simple Sparse Bundle Adjustment).
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SSBA is free software: you can redistribute it and/or modify it under the
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terms of the GNU Lesser General Public License as published by the Free
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Software Foundation, either version 3 of the License, or (at your option) any
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later version.
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SSBA is distributed in the hope that it will be useful, but WITHOUT ANY
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WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
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A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
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details.
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You should have received a copy of the GNU Lesser General Public License along
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with SSBA. If not, see <http://www.gnu.org/licenses/>.
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*/
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