Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video

Garg, Ruchi; Roussos, Anastasios; Agapito, Lourdes

doi:10.1109/cvpr.2013.168

Cited by 201 publications

(385 citation statements)

References 25 publications

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“…Note however that the performance of this algorithm drops significantly, even without the presence of outliers. This is due, as pointed in (Garg et al 2013), to the fact that trajectory-based algorithms become unstable when dealing with small camera rotations, as is the case of this experiment. Similar results are obtained using CSF2 (Gotardo and Martínez 2011b).…”

Section: Paper Bending Sequencementioning

confidence: 82%

“…For this purpose we present the Beating Heart sequence, consisting of 79 frames, acquired during bypass surgery, and which was provided by Garg et al (2013). We use a sparse version of 50 feature points to show the generality of our approach.…”

Section: Beating Heart Sequencementioning

confidence: 99%

“…Stronger deformations have been tackled using piecewise models (Chhatkuli et al 2014;Fayad et al 2010;Russell et al 2011;Taylor et al 2010), even combining segmentation and reconstruction under local rigidity (Russell et al 2014), or eliminating the rank dependency by means of Procustean normal distributions (Lee et al 2013). In (Garg et al 2013), a variational approach integrating a low-rank shape model with spatial smoothness allowed per-pixel dense reconstructions.…”

Section: Related Workmentioning

confidence: 99%

“…This is in contrast to most existing NRSfM approaches, that typically apply constraints in a global manner, usually in the form of low-rank models. For batch methods (Dai et al 2012;Del Bue et al 2006;Garg et al 2013;Torresani et al 2008), these low-rank models are learned after processing all frames of the sequence at once. For sequential ones (Paladini et al 2010;Tao et al 2013), the lowrank model is incrementally learned.…”

Section: Global Statistical Modelmentioning

confidence: 99%

“…This is typically used with additional smoothness constraints that further disambiguate the problem (Bartoli et al 2008;Garg et al 2013;Paladini et al 2009). Yet, while low-rank meth-ods can effectively encode global deformations, they cannot, in general, handle non-linear motion patterns and strong local deformations.…”

mentioning

confidence: 99%

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Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion

Agudo

Moreno-Noguer

2016

Int J Comput Vis

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In this paper, we simultaneously estimate camera pose and non-rigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global low-rank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our on-line methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.

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Section: Paper Bending Sequencementioning

confidence: 82%

Section: Beating Heart Sequencementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Global Statistical Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion

Agudo

Moreno-Noguer

2016

Int J Comput Vis

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Improved Time of Arrival measurement model for non‐convex optimization

et al. 2019

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The quadratic system provided by the Time of Arrival technique can be solved analytically or by nonlinear least squares minimization. An important problem in quadratic optimization is the possible convergence to a local minimum, instead of the global minimum. This problem does not occur for Global Navigation Satellite Systems (GNSS), due to the known satellite positions. In applications with unknown positions of the reference stations, such as indoor localization with self-calibration, local minima are an important issue. This article presents an approach showing how this risk can be significantly reduced. The

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