Conjugate Gradient Bundle Adjustment

Byröd, Martin; Åström, Kalle

doi:10.1007/978-3-642-15552-9_9

Cited by 71 publications

(42 citation statements)

References 16 publications

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“…One common choice is the Block-Jacobian conditioner (Wu et al, 2011, Byröd andÅström, 2010, Agarwal et al, 2010, which is easy to invert since the blocks U and V are block diagonal (Triggs et al, 2000).…”

Section: Conditioningmentioning

confidence: 99%

“…The inverse is easy to compute since the blocks of the diagonal are block diagonal (Byröd andÅström, 2010).…”

Section: Conditioningmentioning

confidence: 99%

“…solved by CG (with block-Jacobian conditioning) and the Hessian or Schur complement matrix is calculated implicitly in both cases. Differences are the computation of dot(p, J T Jp), which PBA calculates as dot(Jp, Jp) (Byröd andÅström, 2010) and a different parametrization of the Jacobian with respect to rotations. Since PBA does not allow an optimization of the same radial distortion parameters, it is only applied in the "metric" case (left column of Figure 2).…”

Section: Data and Test Systemmentioning

confidence: 99%

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Modern Methods of Bundle Adjustment on the Gpu

Hänsch¹,

Drude²,

Hellwich³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The task to compute 3D reconstructions from large amounts of data has become an active field of research within the last years. Based on an initial estimate provided by structure from motion, bundle adjustment seeks to find a solution that is optimal for all cameras and 3D points. The corresponding nonlinear optimization problem is usually solved by the Levenberg-Marquardt algorithm combined with conjugate gradient descent. While many adaptations and extensions to the classical bundle adjustment approach have been proposed, only few works consider the acceleration potentials of GPU systems. This paper elaborates the possibilities of timeand space savings when fitting the implementation strategy to the terms and requirements of realizing a bundler on heterogeneous CPU-GPU systems. Instead of focusing on the standard approach of Levenberg-Marquardt optimization alone, nonlinear conjugate gradient descent and alternating resection-intersection are studied as two alternatives. The experiments show that in particular alternating resection-intersection reaches low error rates very fast, but converges to larger error rates than Levenberg-Marquardt. PBA, as one of the current state-of-the-art bundlers, converges slower in 50% of the test cases and needs 1.5-2 times more memory than the LevenbergMarquardt implementation.

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Section: Conditioningmentioning

confidence: 99%

“…The inverse is easy to compute since the blocks of the diagonal are block diagonal (Byröd andÅström, 2010).…”

Section: Conditioningmentioning

confidence: 99%

Section: Data and Test Systemmentioning

confidence: 99%

See 1 more Smart Citation

Modern Methods of Bundle Adjustment on the Gpu

Hänsch¹,

Drude²,

Hellwich³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Fast Bundle Adjustment Many methods have been proposed that approximate the sparse bundle adjustment and exploit many-core architectures providing significant speed up [2,17,18,19]. Wu [6] show that the recovered structures become stable as they grow larger and require fewer iterations of full-BA.…”

Section: Background and Related Workmentioning

confidence: 99%

Multistage SFM: Revisiting Incremental Structure from Motion

Shah

Deshpande

Narayanan

2014

2014 2nd International Conference on 3D Vision

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In this paper, we present a new multistage approach for SfM reconstruction of a single component. Our method begins with building a coarse 3D reconstruction using highscale features of given images. This step uses only a fraction of features and is fast. We enrich the model in stages by localizing remaining images to it and matching and triangulating remaining features. Unlike traditional incremental SfM, localization and triangulation steps in our approach are made efficient and embarrassingly parallel using geometry of the coarse model. The coarse model allows us to use 3D-2D correspondences based direct localization techniques to register remaining images. We further utilize the geometry of the coarse model to reduce the pair-wise image matching effort as well as to perform fast guided feature matching for majority of features. Our method produces similar quality models as compared to incremental SfM methods while being notably fast and parallel. Our algorithm can reconstruct a 1000 images dataset in 15 hours using a single core, in about 2 hours using 8 cores and in a few minutes by utilizing full parallelism of about 200 cores.

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“…This could be sped up by utilizing the sparse secondary structure of bundle adjustment as shown in [9] or by optimizing the global system using faster iterations that converge slower, e.g. by using conjugate gradient bundle adjustment ( [2]). …”

Section: A Conclusionmentioning

confidence: 99%

Using depth in visual simultaneous localisation and mapping

Scherer

Dubé

Zell

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-We present a method of utilizing depth information as provided by RGBD sensors for robust real-time visual simultaneous localisation and mapping (SLAM) by augmenting monocular visual SLAM to take into account depth data. This is implemented based on the feely available software "Parallel Tracking and Mapping" by Georg Klein. Our modifications allow PTAM to be used as a 6D visual SLAM system even without any additional information about odometry or from an inertial measurement unit.

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Conjugate Gradient Bundle Adjustment

Cited by 71 publications

References 16 publications

Modern Methods of Bundle Adjustment on the Gpu

Modern Methods of Bundle Adjustment on the Gpu

Multistage SFM: Revisiting Incremental Structure from Motion

Using depth in visual simultaneous localisation and mapping

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