Structure- and motion-adaptive regularization for high accuracy optic flow

Wedel, Andreas; Cremers, Daniel; Pock, Thomas; Bischof, Horst

doi:10.1109/iccv.2009.5459375

Cited by 150 publications

(106 citation statements)

References 14 publications

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“…We address the problem of noisy data and model deviations by applying robust norms in E D and E L . Those problems have also been addressed by other authors by introducing a data-based adaption of the smoothness term to rigid motion [22]. In some cases violations of the brightness constancy constraint can be effectively handled by introducing gradient constancy into E D [23].…”

Section: Expected Problemsmentioning

confidence: 94%

Surface tracking assessment and interaction in texture space

Furch¹,

Hilsmann²,

Eisert³

2017

Comp. Visual Media

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In this paper, we present a novel approach for assessing and interacting with surface tracking algorithms targeting video manipulation in postproduction. As tracking inaccuracies are unavoidable, we enable the user to provide small hints to the algorithms instead of correcting erroneous results afterwards. Based on 2D mesh warp-based optical flow estimation, we visualize results and provide tools for user feedback in a consistent reference system, texture space. In this space, accurate tracking results are reflected by static appearance, and errors can easily be spotted as apparent change. A variety of established tools can be utilized to visualize and assess the change between frames. User interaction to improve tracking results becomes more intuitive in texture space, as it can focus on a small region rather than a moving object. We show how established tools can be implemented for interaction in texture space to provide a more intuitive interface allowing more effective and accurate user feedback.

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Section: Expected Problemsmentioning

confidence: 94%

Surface tracking assessment and interaction in texture space

Furch¹,

Hilsmann²,

Eisert³

2017

Comp. Visual Media

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“…Optical flow is often investigated in a variational setting, e.g. [16,20]. Some approaches, however, also employ triangle meshes, for example the discrete algorithm by [9] or the finite element approach of [5].…”

Section: Related Workmentioning

confidence: 99%

Deformable image alignment as a source of stereo correspondences on portraits

Schneider

Kettern

Hilsmann

et al. 2011

CVPR 2011 Workshops

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Mesh-based deformable image alignment (MDIA) is an algorithm that warps a template image onto a target by deforming a 2D control mesh in the image plane, using an image-based nonlinear optimization strategy. MDIA has been successfully applied to various nonrigid registration problems, deformable surface tracking and stabilization of scene-to-camera motion in video. In this paper we investigate the use of image-based MDIA for computing dense correspondences for 3D reconstruction of human heads from high resolution portrait images. Human heads are topologically simple in 3D while providing textures which are challenging to match, such as hair and skin. We find that even with a simple piecewise affine deformation model MDIA delivers excellent correspondence results. We propose a robust, piecewise optimization scheme to compute MDIA on very high resolution images. We address issues of regularization and luminance correction and discuss the role of epipolar constraints. The correspondences retrieved with our approach facilitate the estimation of camera extrinsics and yield highly detailed meshes of the head.

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“…To optimize, we propose to decompose the problem into a sequence of simpler ones, while each subproblem involves alternating updates and iterating until convergence, similar to the quadratic splitting scheme commonly used in recent optical flow works [11,13,14]. Specifically, our algorithm proceeds with the initial u = u 0 and the following iterations:…”

Section: Sequential Optimizationmentioning

confidence: 99%

“…Among them, the TV-L1 framework [11,10] is a popular one, which used total variation (TV) like regularization and a robust L 1 norm in the data term. Based on the observation that motion discontinuities often coincide with object boundaries in images, some researchers proposed to adapt the isotropic spatial regularization to local image structures [13,6]. For data similarity measures, more advanced ones such as image gradient [4] and normalized cross correlation [16,17], have also been proposed to improve over image intensities.…”

Section: Introductionmentioning

confidence: 99%