Toward multiview registration in frame space

Sharp, G; Lee, S.W.; Wehe, D.K.

doi:10.1109/robot.2001.933166

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…This rotation averaging task can be performed by distributing the error along all cycles in a cycle basis, as done by Sharp et al [28] for the alignment of range scans. Approximate solution using least square minimization for multi-view registration is proposed by Govindu [10], reused by Martinec et al [18], and extended with semi-definite programming [3].…”

Section: Related Workmentioning

confidence: 99%

Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion

Moulon

Monasse

Marlet

2013

2013 IEEE International Conference on Computer Vision

303

276

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Multi-view structure from motion (SfM) estimates the position and orientation of pictures in a common 3D coordinate frame. When views are treated incrementally, this external calibration can be subject to drift, contrary to global methods that distribute residual errors evenly. We propose a new global calibration approach based on the fusion of relative motions between image pairs. We improve an existing method for robustly computing global rotations. We present an efficient a contrario trifocal tensor estimation method, from which stable and precise translation directions can be extracted. We also define an efficient translation registration method that recovers accurate camera positions. These components are combined into an original SfM pipeline. Our experiments show that, on most datasets, it outperforms in accuracy other existing incremental and global pipelines. It also achieves strikingly good running times: it is about 20 times faster than the other global method we could compare to, and as fast as the best incremental method. More importantly, it features better scalability properties.

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Section: Related Workmentioning

confidence: 99%

Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion

Moulon

Monasse

Marlet

2013

2013 IEEE International Conference on Computer Vision

303

276

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“…As the global pose remains defined up to an unknown scale factor, we additionally set λ 12 = 1: distances are thus defined with unit length λ 12 . (In case of a cycle, we could also include epipolar constraints to close the loop and distribute errors as in [35], but we do not in our implementation.) Finally, a bundle adjustment refine the initial pose estimation.…”

Section: From Relative To Global Pose Estimationmentioning

confidence: 99%

“…In the following, we only consider the case of a single chain or cycle of cameras, for which we estimate global poses (R j , T j ), where rotations R j , translations T j as well as camera centers C j are defined in the same reference frame. Yet, our pose estimation method can be integrated in a general global SfM framework for arbitrary graphs, e.g., as described in [35] to evenly distribute errors over the whole graph in trying to satisfy the coherency constraints:…”

Section: From Relative To Global Pose Estimationmentioning

confidence: 99%

Line-Based Robust SfM with Little Image Overlap

Salaün

Marlet

Monasse

2017

2017 International Conference on 3D Vision (3DV)

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Usual Structure-from-Motion (SfM) techniques require at least trifocal overlaps to calibrate cameras and reconstruct a scene. We consider here scenarios of reduced image sets with little overlap, possibly as low as two images at most seeing the same part of the scene. We propose a new method, based on line coplanarity hypotheses, for estimating the relative scale of two independent bifocal calibrations sharing a camera, without the need of any trifocal information or Manhattan-world assumption. We use it to compute SfM in a chain of up-to-scale relative motions. For accuracy, we however also make use of trifocal information for line and/or point features, when present, relaxing usual trifocal constraints. For robustness to wrong assumptions and mismatches, we embed all constraints in a parameterless RANSAC-like approach. Experiments show that we can calibrate datasets that previously could not, and that this wider applicability does not come at the cost of inaccuracy.

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Elastic View Graphs: A new Framework for Sequential 3D-SLAM

Kohlhepp

Walther

2006

Autonome Mobile Systeme 2005

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Toward multiview registration in frame space

Cited by 6 publications

References 15 publications

Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion

Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion

Line-Based Robust SfM with Little Image Overlap

Elastic View Graphs: A new Framework for Sequential 3D-SLAM

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