Generalized Thin-Plate Spline Warps

Bartoli, Adrien; Perriollat, Mathieu; Chambon, Sylvie

doi:10.1007/s11263-009-0303-4

Cited by 45 publications

(37 citation statements)

References 28 publications

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“…For smooth deformations of deformable surfaces without crumpling, the bending energy is a well suited constraint [1]. It penalizes the variations of the second order derivatives and is defined as:…”

Section: Priorsmentioning

confidence: 99%

Combining Features and Intensity for Wide-Baseline Non-Rigid Surface Registration

Braux-Zin¹,

Dupont²,

Bartoli³

2013

Procedings of the British Machine Vision Conference 2013

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Non-rigid surface registration consists in estimating the deformation of a known flat surface, usually by fitting a warp such as a Thin-Plate Spline or a Free-Form Deformation. Common techniques are split in two categories: (1) pixel-based surface tracking where important deformations can be estimated over a video sequence as long as the frame to frame steps are small and (2) wide-baseline feature-based registration, able to directly handle large deformations at the cost of reduced accuracy. We introduce a new direct data term robustly merging feature and pixel-based costs in a pyramidal variational approach. By using a robust estimator we achieve an implicit optimal filtering of features and automatic balancing between the two terms.

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Section: Priorsmentioning

confidence: 99%

Combining Features and Intensity for Wide-Baseline Non-Rigid Surface Registration

Braux-Zin¹,

Dupont²,

Bartoli³

2013

Procedings of the British Machine Vision Conference 2013

Self Cite

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“…It should also be noted that these approaches do not evaluate whether specific points can be tracked consistently along the contours, but only focus on the shape. The tracking of 2D deformable surfaces has been studied in [2,18]. But compared to a deformable thread, such objects provide more visual information and often less deformations.…”

Section: Related Workmentioning

confidence: 99%

Deformable Tracking of Textured Curvilinear Objects

Padoy¹,

Hager²

2012

Procedings of the British Machine Vision Conference 2012

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The evaluation and automation of tasks involving the manipulation of deformable curvilinear objects, such as threads and cables, requires the real-time estimation of the 3D shapes of these objects from images. This estimation is however extremely challenging due to the small amount of available visual information, the inherent geometric ambiguities, and the large object deformations.We propose an approach for tracking deformable curvilinear objects using solely visual information from one or more calibrated cameras. The key idea is to formulate the shape estimation as a deformable 1D template tracking problem. The object is first textured with a pattern of different alternating colors. The tracking problem is then expressed as an energy minimization over a set of control points parameterizing a 3D NURBS modeling the object. Assuming the object's in-extensibility, we propose a novel energy based on a texture-sensitive distance map.We demonstrate the benefits of this energy in synthetic and real experiments, using data illustrating the deformation and manipulation of a thread with a da Vinci robot. In particular, we show that the approach allows for deformable tracking in the absence of normal motion along the curve, a challenging practical situation that occurs when the thread is dragged by one extremity.

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“…We use a parametric warp based on Radial Basis Functions (RBF), concretely the Thin Plate Spline (TPS), that minimizes the integral bending energy. For more details about the TPS warp, see [4,5].…”

Section: Feature-based Deformable Surface Detectionmentioning

confidence: 99%

“…We use an outlier rejection method [26] to obtain a set of clean-up matches between each detected template and the objects present in the 2D input image. For those objects that have a number of clean-up matches higher than a defined threshold, we compute an image warp [5,11]. Each warp encodes the particular deformation of an object in the image.…”

Section: Introductionmentioning

confidence: 99%

Deformable 3D Reconstruction with an Object Database

Alcantarilla¹,

Bartoli²

2012

Procedings of the British Machine Vision Conference 2012

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Deformable 3D reconstruction from 2D images requires prior knowledge on the scene structure. Template-free methods use generic prior knowledge such as piecewise smoothness but require multiple images with significant baseline. Template-based methods require only one image but handle only one object for which they need specific prior knowledge, namely a 3D template. We here propose a novel method that alleviates the strong assumptions of both the template-free and template-based methods: our method uses multiple templates to achieve deformable 3D reconstruction from only one image and for multiple objects. It uses object recognition to automatically discover what objects are visible in the input image and to select the appropriate templates for deformable 3D reconstruction. The object database is built offline. Crucially, this database does not only contain appearance descriptors as in existing object recognition frameworks, but also material properties to facilitate deformable 3D reconstruction. We show successful experimental results with objects made of various materials such as paper, cloth and plastic.

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Generalized Thin-Plate Spline Warps

Cited by 45 publications

References 28 publications

Combining Features and Intensity for Wide-Baseline Non-Rigid Surface Registration

Combining Features and Intensity for Wide-Baseline Non-Rigid Surface Registration

Deformable Tracking of Textured Curvilinear Objects

Deformable 3D Reconstruction with an Object Database

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