3D Body Registration from RGB-D Data with Unconstrained Movements and Single Sensor

Villena-Martínez, Víctor; Fuster-Guilló, Andrés; Saval-Calvo, Marcelo; Azorín-López, Jorge

doi:10.1007/978-3-319-59147-6_28

Cited by 4 publications

(7 citation statements)

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“…The contributions of this paper are: (1) we provide a global overview of learning-based registration methods by proposing a well-defined framework that encompasses both the traditional and learning-based approaches; and (2) we review the recent learning-based registration approaches, which have been classified according to a proposed taxonomy to foster discussion. Figure 1 graphically summarizes the scope of this paper.…”

Section: Review Scopementioning

confidence: 99%

“…Here, we consider both 2D and 3D data, and data in point sets, grids, and meshes. Rigid and non-rigid registration has already been widely addressed in the computer vision literature through potential applications mostly for data analysis such as body modeling [1] for pose analysis; computed tomography registration [2,3] for medical diagnosis; multi-camera registration for robot guidance [4]; and applications in object classification on assembly lines [5], among others. In the aforementioned applications, registration represents a crucial component.…”

Section: Introductionmentioning

confidence: 99%

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When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

et al. 2020

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This paper reviews recent deep learning-based registration methods. Registration is the process that computes the transformation that aligns datasets, and the accuracy of the result depends on multiple factors. The most significant factors are the size of input data; the presence of noise, outliers and occlusions; the quality of the extracted features; real-time requirements; and the type of transformation, especially those defined by multiple parameters, such as non-rigid deformations. Deep Registration Networks (DRNs) are those architectures trying to solve the alignment task using a learning algorithm. In this review, we classify these methods according to a proposed framework based on the traditional registration pipeline. This pipeline consists of four steps: target selection, feature extraction, feature matching, and transform computation for the alignment. This new paradigm introduces a higher-level understanding of registration, which makes explicit the challenging problems of traditional approaches. The main contribution of this work is to provide a comprehensive starting point to address registration problems from a learning-based perspective and to understand the new range of possibilities.

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Section: Review Scopementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

et al. 2020

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“…An iterative low-cost method for 3D body registration, dealing with unconstrained movements and accuracy is suggested in [37]. A novel method for 3D object reconstruction from RGB-D data that applies sub mapping to 3D bundle adjustment is presented in [38].…”

Section: Related Workmentioning

confidence: 99%

Accuracy analysis of 3D object reconstruction using RGB-D sensor

Ruchay

Dorofeev

Kober³

2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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The geometry of an object can be reconstructed from laser range scans, a set of different photos, monocular cameras and stereo cameras; each technology possesses disadvantages and limitations. RGB-D cameras like Microsoft Kinect or ASUS Xtion Pro are sensing systems equipped with an RGB camera, infrared projector, and infrared sensor. They can gather RGB information and the depth map simultaneously. The ICP (Iterative Closest Point) is one of the most commonly used methods for pairwise alignment, where the rotation and translation to align two point clouds are determined. Many variants on the base ICP algorithm were proposed because of the limitations in the ICP algorithm: different cost functions [11] and others [12]; two-Pass ICP with color constraint to improve the error minimization process [13]; 3D representation using a set of planes to perform the registration [14]; point-to-plane matchings instead of point-to-point matchings in standard

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“…This is what we know in computer vision and pattern recognition literature as point set registration -the process of finding a spatial transformation that aligns two points sets-, and, in a more general way, to align two data sets. Examples where registration is used are uncountable, but for in-stance we can name animation [1]; body modeling [2] for pose analysis; medical diagnosis, for example, Zeman et al [3] that registered Computed Tomography or Boldea et al [4] that employed 3D models; robot guidance, e.g. registered multi-camera setup to guide a robot arm [5]; object classification of assembly lines [6], etc.…”

Section: Introductionmentioning

confidence: 99%

“…The current context is changing the predominant paradigm in registration techniques in two ways: (1) dealing with a huge amount of raw and unstructured multidimensional data is not straightforward, especially if we have to meet real-time constraints; (2) in light of the success of Deep Learning (DL) in the computer vision field, the large amount of available data satisfies the needs of DLbased approaches which are well-known to be data hungry. This opens a promising avenue in the research of registration methods using DL techniques.…”

Section: Introductionmentioning

confidence: 99%

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

Villena-Martinez,

Oprea,

Saval-Calvo

et al. 2020

Preprint

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Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input data, the presence of noise, outliers and occlusions, the quality of the extracted features, real-time requirements and the type of transformation, especially those ones defined by multiple parameters, like non-rigid deformations.Recent advancements in machine learning could be a turning point in these issues, particularly with the development of deep learning (DL) techniques, which are helping to improve multiple computer vision problems through an abstract understanding of the input data. In this paper, a review of deep learning-based registration methods is presented. We classify the different papers proposing a framework extracted from the traditional registration pipeline to analyse the new learning-based proposal strengths. Deep Registration Networks (DRNs) try to solve the alignment task either replacing part of the traditional pipeline with a network or fully solving the registration problem. The main conclusions extracted are, on the one hand, 1) learning-based registration techniques cannot always be clearly classified in the traditional pipeline. 2) These approaches allow more complex inputs like conceptual models as well as the traditional 3D datasets. 3) In spite of the generality of learning, the current proposals are still ad hoc solutions. Finally, 4) this is a young topic that still requires a large effort to reach general solutions able to cope with the problems that affect traditional approaches.

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3D Body Registration from RGB-D Data with Unconstrained Movements and Single Sensor

Cited by 4 publications

References 24 publications

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

Accuracy analysis of 3D object reconstruction using RGB-D sensor

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

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