A Global Linear Method for Camera Pose Registration

Jiang, Nan; Cui, Zhaopeng; Tan, Ping

doi:10.1109/iccv.2013.66

Cited by 141 publications

(164 citation statements)

References 27 publications

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“…The initial orientation parameters computed in this way, however, depend on the initial sub-set and the ordering in which images are added and may lead to a poor overall accuracy of the final image orientations. This is why global methods recently gained a lot of attention (Martinec and Pajdla, 2007), (Enqvist et al, 2011), (Arie-Nachimson et al, 2012), (Sinha et al, 2012), (Jiang et al, 2013), (Moulon et al, 2013). Often these approaches work in a two-step manner, first estimating absolute rotations and then translations and/or object points.…”

Section: Related Workmentioning

confidence: 99%

“…tie points in overlapping images, and relative rotations between pairs of images and is independent on initial values for the unknowns. Following the common terminology we call this non-sequential approach a global approach (Arie-Nachimson et al, 2012), (Jiang et al, 2013), (Moulon et al, 2013). The workflow for the estimation of image orientation parameters is outlined in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Global Rotation Estimation Using Weighted Iterative Lie Algebraic Averaging

Reich

Heipke

2015

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

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ABSTRACT:In this paper we present an approach for a weighted rotation averaging to estimate absolute rotations from relative rotations between two images for a set of multiple overlapping images. The solution does not depend on initial values for the unknown parameters and is robust against outliers. Our approach is one part of a solution for a global image orientation. Often relative rotations are not free from outliers, thus we use the redundancy in available pairwise relative rotations and present a novel graph-based algorithm to detect and eliminate inconsistent rotations. The remaining relative rotations are input to a weighted least squares adjustment performed in the Lie algebra of the rotation manifold SO(3) to obtain absolute orientation parameters for each image. Weights are determined using the prior information we derived from the estimation of the relative rotations. Because we use the Lie algebra of SO (3) for averaging no subsequent adaptation of the results has to be performed but the lossless projection to the manifold. We evaluate our approach on synthetic and real data. Our approach often is able to detect and eliminate all outliers from the relative rotations even if very high outlier rates are present. We show that we improve the quality of the estimated absolute rotations by introducing individual weights for the relative rotations based on various indicators. In comparison with the state-of-the-art in recent publications to global image orientation we achieve best results in the examined datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global Rotation Estimation Using Weighted Iterative Lie Algebraic Averaging

Reich

Heipke

2015

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

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“…However, this two-step approach cannot deal with images that have been captured from an almost linear trajectory -those images will be denoted here forth as linear-trajectory images. Jiang et al (2013) proposed another two-step global linear method for the estimation of EOPs of available images. To handle lineartrajectory images, they use the baseline/depth ratios from neighbouring stereo-pairs for the image augmentation process.…”

Section: Related Workmentioning

confidence: 99%

Linear Approach for Initial Recovery of the Exterior Orientation Parameters of Randomly Captured Images by Low-Cost Mobile Mapping Systems

Habib

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, WG I/3KEY WORDS: Exterior Orientation, Relative Orientation, Linear Approaches, Bundle Adjustment ABSTRACT:In this paper, we present a novel linear approach for the initial recovery of the exterior orientation parameters (EOPs) of images. Similar to the conventional Structure from Motion (SfM) algorithm, the proposed approach is based on a two-step strategy. In the first step, the relative orientation of all possible image stereo-pairs is estimated. In the second step, a local coordinate frame is established, and an incremental image augmentation process is implemented to reference all the remaining images into a local coordinate frame. Since our approach is based on a linear solution for both the relative orientation estimation as well as the initial recovery of the image EOPs, it does not require any initial approximation for the optimization process. Another advantage of our approach is that it does not require any prior knowledge regarding the sequence of the image collection procedure, therefore, it can handle a set of randomly collected images in the absence of GNSS/INS information. In order to illustrate the feasibility of our approach, several experimental tests are conducted on real datasets captured in either a block or linear trajectory configuration. The results demonstrate that the initial image EOPs obtained are accurate and can serve as a good initialization for an additional bundle adjustment process.

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“…For inexperienced users, the requirements for collecting suitable imagery for "conventional" photogrammetry are difficult to accomplish. As such, structure from motion (SfM) in computer vision is applied to reconstruct a 3D model by using irregular image data (Agarwal et al, 2011;Jiang et al, 2013;Schaffalitzky and Zisserman, 2002;Strecha et al, 2008). SfM is driven by enabling automated model reconstruction from imagery with few limitations in types of digital cameras and viewing angles (Uysal et al, 2015;Westoby et al, 2012).With the advantages of the modern digital camera technology, nonmetric cameras, such as digital single-lens reflex cameras (DSLRs), are widely used, particularly on the platform of UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, coarse estimation for image network is significant for the final self-calibration bundle adjustment, when only approximate camera parameters can be extracted from image files without distortion information. Moreover, continuous estimation of camera model parameters when establishing image network with incremental SfM method is time consuming (Jiang et al, 2013;Wilson and Snavely, 2014).…”

Section: Introductionmentioning

confidence: 99%

Image Network Generation of Uncalibrated Uav Images With Low-Cost GPS Data

Huang

Zhang

Jianan

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

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A Global Linear Method for Camera Pose Registration

Cited by 141 publications

References 27 publications

Global Rotation Estimation Using Weighted Iterative Lie Algebraic Averaging

Global Rotation Estimation Using Weighted Iterative Lie Algebraic Averaging

Linear Approach for Initial Recovery of the Exterior Orientation Parameters of Randomly Captured Images by Low-Cost Mobile Mapping Systems

Image Network Generation of Uncalibrated Uav Images With Low-Cost GPS Data

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