Fast Automatic Differentiation for Large Scale Bundle Adjustment

The multiview 3D data registration precision will decrease with the increasing number of registrations when measuring a large scale object using structured light scanning. In this paper, we propose a high-precision registration method based on multiple view geometry theory in order to solve this problem. First, a multiview network is constructed during the scanning process. The bundle adjustment method from digital close range photogrammetry is used to optimize the multiview network to obtain high-precision global control points. After that, the 3D data under each local coordinate of each scan are registered with the global control points. The method overcomes the error accumulation in the traditional registration process and reduces the time consumption of the following 3D data global optimization. The multiview 3D scan registration precision and efficiency are increased. Experiments verify the effectiveness of the proposed algorithm.

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Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽

10.3390/s21041091 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1091

Author(s):

Izaak Van Crombrugge ◽

Rudi Penne ◽

Steve Vanlanduit

Keyword(s):

Camera Calibration ◽

Real World ◽

Large Scale ◽

State Of The Art ◽

Bundle Adjustment ◽

Field Of View ◽

Extrinsic Calibration ◽

Practical Procedure ◽

Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

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Fast automatic differentiation as applied to the computation of second derivatives of composite functions

Computational Mathematics and Mathematical Physics ◽

10.1134/s0965542506110029 ◽

2006 ◽

Vol 46 (11) ◽

pp. 1835-1859

Author(s):

E. S. Zasukhina

Keyword(s):

Automatic Differentiation ◽

Fast Automatic Differentiation ◽

Composite Functions ◽

Second Derivatives ◽

Derivatives Of

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INVESTIGATIONS ON THE BUNDLE ADJUSTMENT RESULTS FROM SFM-BASED SOFTWARE FOR MAPPING PURPOSES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-623-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 623-628

Author(s):

Y. A. Lumban-Gaol ◽

A. Murtiyoso ◽

B. H. Nugroho

Keyword(s):

Aerial Photography ◽

Large Scale ◽

Bundle Adjustment ◽

Aerial Images ◽

Topographic Maps ◽

Main Method ◽

Aerial Photogrammetry ◽

Lower Accuracy ◽

Spatial Management ◽

Erdas Imagine

Since its first inception, aerial photography has been used for topographic mapping. Large-scale aerial photography contributed to the creation of many of the topographic maps around the world. In Indonesia, a 2013 government directive on spatial management has re-stressed the need for topographic maps, with aerial photogrammetry providing the main method of acquisition. However, the large need to generate such maps is often limited by budgetary reasons. Today, SfM (Structure-from-Motion) offers quicker and less expensive solutions to this problem. However, considering the required precision for topographic missions, these solutions need to be assessed to see if they provide enough level of accuracy. In this paper, a popular SfM-based software Agisoft PhotoScan is used to perform bundle adjustment on a set of large-scale aerial images. The aim of the paper is to compare its bundle adjustment results with those generated by more classical photogrammetric software, namely Trimble Inpho and ERDAS IMAGINE. Furthermore, in order to provide more bundle adjustment statistics to be compared, the Damped Bundle Adjustment Toolbox (DBAT) was also used to reprocess the PhotoScan project. Results show that PhotoScan results are less stable than those generated by the two photogrammetric software programmes. This translates to lower accuracy, which may impact the final photogrammetric product.

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Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment

Lecture Notes in Computer Science - Outdoor and Large-Scale Real-World Scene Analysis ◽

10.1007/978-3-642-34091-8_6 ◽

2012 ◽

pp. 131-150 ◽

Cited By ~ 16

Author(s):

Yong-Dian Jian ◽

Doru C. Balcan ◽

Frank Dellaert

Keyword(s):

Large Scale ◽

Bundle Adjustment

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A Flow Perspective on Nonlinear Least-Squares Problems

Vietnam Journal of Mathematics ◽

10.1007/s10013-020-00441-z ◽

2020 ◽

Vol 48 (4) ◽

pp. 987-1003

Author(s):

Hans Georg Bock ◽

Jürgen Gutekunst ◽

Andreas Potschka ◽

María Elena Suaréz Garcés

Keyword(s):

Least Squares ◽

Newton Method ◽

Large Scale ◽

Gradient Flow ◽

Nonlinear Least Squares ◽

Bundle Adjustment ◽

Least Squares Problems ◽

Flow Equations ◽

Backward Euler ◽

Forward Euler

AbstractJust as the damped Newton method for the numerical solution of nonlinear algebraic problems can be interpreted as a forward Euler timestepping on the Newton flow equations, the damped Gauß–Newton method for nonlinear least squares problems is equivalent to forward Euler timestepping on the corresponding Gauß–Newton flow equations. We highlight the advantages of the Gauß–Newton flow and the Gauß–Newton method from a statistical and a numerical perspective in comparison with the Newton method, steepest descent, and the Levenberg–Marquardt method, which are respectively equivalent to Newton flow forward Euler, gradient flow forward Euler, and gradient flow backward Euler. We finally show an unconditional descent property for a generalized Gauß–Newton flow, which is linked to Krylov–Gauß–Newton methods for large-scale nonlinear least squares problems. We provide numerical results for large-scale problems: An academic generalized Rosenbrock function and a real-world bundle adjustment problem from 3D reconstruction based on 2D images.

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