Degeneracies in Rolling Shutter SfM

Albl, Cenek; Sugimoto, Akihiro; Pajdla, Tomáš

doi:10.1007/978-3-319-46454-1_3

Cited by 26 publications

(43 citation statements)

References 15 publications

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“…For such cases, researchers have derived several practically important cases. Similarly, we derive an intuitive CMS here, which coincides with the one given in [4]. Thus, the following gives yet another explanation of the CMS.…”

Section: Representation Of Cmsssupporting

confidence: 80%

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Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure from Motion

Ito¹,

Okatani

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM. Employing an RS camera model with linearized pure rotation, we show that the RS distortion can be approximately expressed by two internal parameters of an "imaginary" camera plus one-parameter nonlinear transformation similar to lens distortion. We then reformulate the problem as self-calibration of the imaginary camera, in which its skew and aspect ratio are unknown and varying in the image sequence. In the formulation, we derive a general representation of CMSs. We also show that our method can explain the CMS that was recently reported in the literature, and then present a new remedy to deal with the degeneracy. Our theoretical results agree well with experimental results; it explains degeneracies observed when we employ naive bundle adjustment, and how they are resolved by our method.

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Section: Representation Of Cmsssupporting

confidence: 80%

“…This style of capturing images is quite common. Even if a camera motion does not exactly match this CMS, if it is somewhat close to it, estimation accuracy could deteriorate depending on how close it is; see [4] for more detailed discussions.…”

Section: Representation Of Cmssmentioning

confidence: 99%

Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure from Motion

Ito¹,

Okatani

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Cohen et al [58] proposed a novel solution for 3D reconstruction based on SFM to reconstruct the inside and the outside of a building into a single model by utilizing the semantic information, in which, novel cost function is proposed to determine the best alignment. To solve the degeneracies introduced by rolling shutter camera models, Albl et al [59] show that many common camera configurations such as cameras with parallel readout directions, become critical and allow for a large class of ambiguities in 3D reconstruction based on SFM technique.…”

Section: Structure From Motionmentioning

confidence: 99%

Superpixel-Based Feature Tracking for Structure from Motion

Cao

Jia

et al. 2019

Applied Sciences

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Feature tracking in image collections significantly affects the efficiency and accuracy of Structure from Motion (SFM). Insufficient correspondences may result in disconnected structures and incomplete components, while the redundant correspondences containing incorrect ones may yield to folded and superimposed structures. In this paper, we present a Superpixel-based feature tracking method for structure from motion. In the proposed method, we first propose to use a joint approach to detect local keypoints and compute descriptors. Second, the superpixel-based approach is used to generate labels for the input image. Third, we combine the Speed Up Robust Feature and binary test in the generated label regions to produce a set of combined descriptors for the detected keypoints. Fourth, the locality-sensitive hash (LSH)-based k nearest neighboring matching (KNN) is utilized to produce feature correspondences, and then the ratio test approach is used to remove outliers from the previous matching collection. Finally, we conduct comprehensive experiments on several challenging benchmarking datasets including highly ambiguous and duplicated scenes. Experimental results show that the proposed method gets better performances with respect to the state of the art methods.

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“…ing a too complex model may lead to over-fitting the data and result in degeneracies [5], as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Based Camera Model Selection

Polic

Steidl

Albl

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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The quality and speed of Structure from Motion (SfM) methods depend significantly on the camera model chosen for the reconstruction. In most of the SfM pipelines, the camera model is manually chosen by the user. In this paper, we present a new automatic method for camera model selection in large scale SfM that is based on efficient uncertainty evaluation. We first perform an extensive comparison of classical model selection based on known Information Criteria and show that they do not provide sufficiently accurate results when applied to camera model selection. Then we propose a new Accuracy-based Criterion, which evaluates an efficient approximation of the uncertainty of the estimated parameters in tested models. Using the new criterion, we design a camera model selection method and fine-tune it by machine learning. Our simulated and real experiments demonstrate a significant increase in reconstruction quality as well as a considerable speedup of the SfM process.

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Degeneracies in Rolling Shutter SfM

Cited by 26 publications

References 15 publications

Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure from Motion

Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure from Motion

Superpixel-Based Feature Tracking for Structure from Motion

Uncertainty Based Camera Model Selection

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