Real-time detection of planar regions in unorganized point clouds

Limberger, Frederico A.; Oliveira, Manuel M.

doi:10.1016/j.patcog.2014.12.020

Cited by 118 publications

(56 citation statements)

References 39 publications

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“…These planes are used for robot automation [HSSM14] or scene reconstruction [WO02, LO15]. Their main drawback—the size and memory footprint of the parameter space—can be overcome using hierarchical voxel grids depending on the number of point contributions in each cell [HSSM14].…”

Section: Theoretical Foundationsmentioning

confidence: 99%

“…Their main drawback—the size and memory footprint of the parameter space—can be overcome using hierarchical voxel grids depending on the number of point contributions in each cell [HSSM14]. In order to solve processing time issues, Limberger and Oliveira [LO15] apply the Kernel‐based Hough Transform [FO08] to 3D plane detection using an efficient spherical accumulator.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

“…A second family of parameters influence directly the algorithm and are more abstract. Spatial parameters such as the resolution of a voxel grid [FCSS09b] or parameter space [LO15] or the radius of a neighbourhood [VLA15, LM12] are usually quite intuitive. On the other hand, algorithm‐specific values such as the number of iterations [GMLB12], optimization weights [ASF*13, JX13, SXZ*12, WPM*12] or primitive initialization method [CLL11] do not make much sense to the user.…”

Section: Characterizationmentioning

confidence: 99%

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A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

Kaiser

Zepeda

Boubekeur

2018

Computer Graphics Forum

107

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The amount of captured 3D data is continuously increasing, with the democratization of consumer depth cameras, the development of modern multi‐view stereo capture setups and the rise of single‐view 3D capture based on machine learning. The analysis and representation of this ever growing volume of 3D data, often corrupted with acquisition noise and reconstruction artefacts, is a serious challenge at the frontier between computer graphics and computer vision. To that end, segmentation and optimization are crucial analysis components of the shape abstraction process, which can themselves be greatly simplified when performed on lightened geometric formats. In this survey, we review the algorithms which extract simple geometric primitives from raw dense 3D data. After giving an introduction to these techniques, from the acquisition modality to the underlying theoretical concepts, we propose an application‐oriented characterization, designed to help select an appropriate method based on one's application needs and compare recent approaches. We conclude by giving hints for how to evaluate these methods and a set of research challenges to be explored.

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Section: Theoretical Foundationsmentioning

confidence: 99%

Section: Theoretical Foundationsmentioning

confidence: 99%

Section: Characterizationmentioning

confidence: 99%

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A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

Kaiser

Zepeda

Boubekeur

2018

Computer Graphics Forum

107

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“…In low-level feature extraction, the research mostly concentrates on several direction such as edge detection [1,2,3], corner detection [4,5], blob detection [6,7,8,9], ridge detection [10,11], and scale-invariant feature transform (SIFT) [12,13,14,15,16]. In addition, there are also many shaped-based method like, template matching [18,19,20], thresholding [17], and hough transform [21,22,23,24].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Feature Extraction and Image Classification Based on PCNN and Image Quality

Liang¹,

Ma²

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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Abstract. Considering that PCNN is in accordance with human vision, so it has a good performance in image binaryzation and segmentation, the PCNN-based methods have advantage in feature extraction and image classification. In this paper, we propose a new method for feature extraction and image classification based PCNN which is more robust than others. This method mainly analyzes the quality and centroid of the image and extracts features from the relationship between different centroid. Then, the Euclidean distance distribution parameters used to describe these features. In image classification, the feature parameter also play an important role and distinguishes the difference between various object images efficiently.

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“…As estratégias da PHT (Probabilis- (42); têm como foco a redução do tempo de execução através do uso de um subconjunto dos pontos da nuvem de pontos P , em oposição à criação de novos algoritmos que efetivamente venham a reduzir o custo assintótico do processo de votação (1).…”

Section: Detecção De Planosunclassified

Uso De Estruturas Planares Extraídas De Imagens RGB-D Em Aplicações De Realidade Aumentada

SILVA¹

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Real-time detection of planar regions in unorganized point clouds

Cited by 118 publications

References 39 publications

A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

A New Method for Feature Extraction and Image Classification Based on PCNN and Image Quality

Uso De Estruturas Planares Extraídas De Imagens RGB-D Em Aplicações De Realidade Aumentada

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