Taxonomy of 3D Sensors

Schöning, Julius; Heidemann, Gunther

doi:10.5220/0005784801920197

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Active sensors use an active source of energy, i.e., a projector, to emit a laser beam or a pattern of light, typically in the infrared spectrum, and a receiver to capture the light reflected from the objects in the scene. Common active sensors are laser range scanners, and, more recently, consumer grade sensors based on structured light and time-of-flight technologies [11]. Sensors based on structured light and time-of-flight technologies, besides being typically much cheaper than laser scanners, have the advantage of capturing the entire scene at the same time, making them suitable for usage in gait recognition and other dynamic scenes applications.…”

Section: Input Video Stream Processingmentioning

confidence: 99%

Extending Appearance Based Gait Recognition with Depth Data

et al. 2019

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Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.

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Section: Input Video Stream Processingmentioning

confidence: 99%

Extending Appearance Based Gait Recognition with Depth Data

et al. 2019

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“…These solutions require appropriate hardware, together with the specialized software, which gives them a larger overhead, for users to get into. Examples of 3D reconstruction using these methods are extensively benchmarked by (Jamaluddin et al, 2017), (Schöning and Heidemann, 2016).…”

Section: Introduction 1object 3d Reconstructionmentioning

confidence: 99%

Performance Characterization of Absolute Scale Computation for 3D Structure from Motion Reconstruction

Nikolov

Madsen

2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Structure from Motion (SfM) 3D reconstruction of objects and environments has become a go-to process, when detailed digitization and visualization is needed in the energy and production industry, medicine, culture and media. A successful reconstruction must properly capture the 3D information and it must scale everything to the correct scale. SfM has an inherent ambivalence to the scale of the scanned objects, so additional data is needed. In this paper we propose a lightweight solution for computation of absolute scale of 3D reconstructions by using only a real-time kinematic (RTK) GPS, in comparison to other custom solutions, which require multiple sensor fusion. Additionally, our solution estimates the noise sensitivity of the calculated scale, depending on the precision of the positioning sensor. We first test our solution with synthetic data to find how the results depend on changes to the capturing setup. We then test our pipeline using real world data, against the built-in solutions used in two state-of-the-art reconstruction software. We show that our solution gives better results, than both state-of-the-art solutions.

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“…Geometry-based [31][32][33][34][35][36][37][38] Fig. 1 Taxonomical classification of shape analysis techniques. At the root, they can be classified based on dimensionality of the model (2D or 3D).…”

Section: Introductionmentioning

confidence: 99%

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

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Taxonomy of 3D Sensors

Cited by 10 publications

References 19 publications

Extending Appearance Based Gait Recognition with Depth Data

Extending Appearance Based Gait Recognition with Depth Data

Performance Characterization of Absolute Scale Computation for 3D Structure from Motion Reconstruction

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

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