Structure of Applicable Surfaces from Single Views

Gumerov, Nail A.; Zandifar, Ali; Duraiswami, Ramani; Davis, Larry S.

doi:10.1007/978-3-540-24672-5_38

Cited by 50 publications

(31 citation statements)

References 10 publications

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“…This problem can be avoided by computing the 3D shape directly from the correspondences, which amounts to solving a degenerate linear system and requires either reducing the number of degrees of freedom or imposing additional constraints [2]. The first can be achieved by various dimensionality reduction techniques [3], [4] while the second often involves assuming the surface to be either developable [5], [6], [7] or inextensible [8], [9], [10], [4]. These two approaches are sometimes combined and augmented by introducing additional sources of information such as shading or textural clues [11], [12] or physicsbased constraints [13].…”

Section: Introductionmentioning

confidence: 99%

Template-Based Monocular 3D Shape Recovery Using Laplacian Meshes

Ngo

Östlund

Fua

2016

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We show that by extending the Laplacian formalism, which was first introduced in the Graphics community to regularize 3D meshes, we can turn the monocular 3D shape reconstruction of a deformable surface given correspondences with a reference image into a much better-posed problem. This allows us to quickly and reliably eliminate outliers by simply solving a linear least squares problem. This yields an initial 3D shape estimate, which is not necessarily accurate, but whose 2D projections are. The initial shape is then refined by a constrained optimization problem to output the final surface reconstruction. Our approach allows us to reduce the dimensionality of the surface reconstruction problem without sacrificing accuracy, thus allowing for real-time implementations.

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Section: Introductionmentioning

confidence: 99%

Template-Based Monocular 3D Shape Recovery Using Laplacian Meshes

Ngo

Östlund

Fua

2016

IEEE Trans. Pattern Anal. Mach. Intell.

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“…From the experimental figure it can be also seen that there is a better effect when the fitting curve is within 6 m and the trend of fitting error is smaller; along with the increasing distance, the fitting effect has shown a certain deviation and the fitting error is increased. So there is a linear relationship of RSSI value between adjacent distances which is less than 10 m, the not-measured RSSI value can be obtained by using the interpolation [33]. It is known that the real RSSI value is close to Gaussian distribution at different distance by analyzing the above section; the place of larger distribution density of RSSI is most likely to be the distance between receiving nodes and projection nodes.…”

Section: Ranging Methods Based Onmentioning

confidence: 99%

A Location Estimation Algorithm Based on RSSI Vector Similarity Degree

Shang

Wen

Wang

et al. 2014

International Journal of Distributed Sensor Networks

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We present a detailed study on the RSS-based location techniques in wireless sensor networks (WSN). There are two aspects in this paper. On the one hand, the accurate RSSI received from nodes is the premise of accurate location. Firstly, the distribution trend of RSSI is analyzed in this experiment and determined the loss model of signal propagation by processing experimental data. Secondly, in order to determine the distance between receiving nodes and sending nodes, Gaussian fitting is used to process specific RSSI at different distance. Moreover, the piecewise linear interpolation is introduced to calculate the distance of any RSSI. On the other hand, firstly, the RSSI vector similarity degree (R-VSD) is used to choose anchor nodes. Secondly, we designed a new localization algorithm which is based on the quadrilateral location unit by using more accurate RSSI and range. Particularly, there are two localization mechanisms in our study. In addition, the generalized inverse is introduced to solve the coordinates of nodes. At last, location error of the new algorithm is about 17.6% by simulation experiment.

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“…There are in fact relatively few others that can do this for deformable objects. One of them has been proposed in Gumerov et al (2004) but requires that the whole outline be detected, which severely limits its scope. Another is the tracking of Lin and Liu (2006) that exploits the repeating properties of a near regular texture to discover new texture tiles in new frames.…”

Section: Direct Methodsmentioning

confidence: 99%

Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

2007

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Abstract. We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge.Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC. We demonstrate its applicability by using it to realistically modify the texture of a deforming surface and to handle complex illumination effects.Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of more than 90%, which is considerably more than what is required in practice.

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Structure of Applicable Surfaces from Single Views

Cited by 50 publications

References 10 publications

Template-Based Monocular 3D Shape Recovery Using Laplacian Meshes

Template-Based Monocular 3D Shape Recovery Using Laplacian Meshes

A Location Estimation Algorithm Based on RSSI Vector Similarity Degree

Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

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