Real-Time Removing of Outliers and Noise in 3D Point Clouds Applied in Robotic Applications

Arvanitis, Gerasimos; Lalos, Aris S.; Μουστάκας, Κωνσταντίνος; Fakotakis, Nikos

doi:10.1007/978-3-319-66471-2_2

Cited by 9 publications

(17 citation statements)

References 8 publications

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“…• Imaging for 3D computer vision: This application requires mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. Thus, RPCA can also be used to reduce outliers and noise in algorithms such as Structure from Motion (SfM) [177], [291], [9], [8], 3D motion recovery [283], and 3D reconstruction [10].…”

Section: A Image Processingmentioning

confidence: 99%

“…utilizes the fact that the intrinsic image, which reflects the light reflectance properties of a face, is common for the face images taken under different lightings. The decomposition can thus be done by solving the robust PCA problem (10).…”

Section: ) Intrinsic Image Computationmentioning

confidence: 99%

“…However, the sparsest representation (19) is not robust to large contiguous occlusion such as scarf and sunglasses. To mitigate the issue, rather than minimizing the sparse representation model (19), Luan et al [185] proposed an approach for robust face recognition by exploiting the sparse term obtained by robust PCA (10). In particular, they first constructed a matrix of normalized (training and testing) samples by stacking all facial images as the columns of the matrix.…”

Section: ) Multi-focus Image Fusionmentioning

confidence: 99%

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On the Applications of Robust PCA in Image and Video Processing

et al. 2018

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Robust PCA (RPCA) via decomposition into lowrank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a lowrank matrix. Moreover, most of the time experiments show that RPCA with additional spatial and/or temporal constraints often outperforms the state-of-the-art algorithms in these applications. Thus, the aim of this paper is to survey the applications of RPCA in computer vision. In the first part of this paper, we review representative image processing applications as follows: (1) lowlevel imaging such as image recovery and denoising, image composition, image colorization, image alignment and rectification, multi-focus image and face recognition, (2) medical imaging like dynamic Magnetic Resonance Imaging (MRI) for acceleration of data acquisition, background suppression and learning of inter-frame motion fields, and (3) imaging for 3D computer vision with additional depth information like in Structure from Motion (SfM) and 3D motion recovery. In the second part, we present the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing. Specifically, we investigate video denoising and restoration, hyperspectral video and background/foreground separation. Finally, we provide perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.

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Section: A Image Processingmentioning

confidence: 99%

Section: ) Intrinsic Image Computationmentioning

confidence: 99%

Section: ) Multi-focus Image Fusionmentioning

confidence: 99%

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On the Applications of Robust PCA in Image and Video Processing

et al. 2018

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“…where M is the matrix representing the point cloud, L is the low-rank matrix that can be considered to correspond to the inliers, and S is a sparse matrix corresponding to the outliers [6]. A threshold l D2 is used to determine whether the new point coordinates are close enough to the inliers.…”

Section: Point Position Improvementmentioning

confidence: 99%

“…Outliers are removed in [5] by employing a statistical analysis of the points' geometrical properties, while the approach in [3] employs geometric and photometric consistency measures. The method in [6] removes outliers using Robust Principal Component Analysis (RPCA) and denoises the resulting point cloud with a bilateral filter [7,8]. However, these methods are usually designed in the context of improving a particular task in a 3D reconstruction pipeline, such as surface reconstruction, or make explicit assumptions on the input scene.…”

Section: Introductionmentioning

confidence: 99%

Denoising of 3D Point Clouds Constructed from Light Fields

Galea

Guillemot

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Light fields are 4D signals capturing rich information from a scene. The availability of multiple views enables scene depth estimation, that can be used to generate 3D point clouds. The constructed 3D point clouds, however, generally contain distortions and artefacts primarily caused by inaccuracies in the depth maps. This paper describes a method for noise removal in 3D point clouds constructed from light fields. While existing methods discard outliers, the proposed approach instead attempts to correct the positions of points, and thus reduce noise without removing any points, by exploiting the consistency among views in a light-field. The proposed 3D point cloud construction and denoising method exploits uncertainty measures on depth values. We also investigate the possible use of the corrected point cloud to improve the quality of the depth maps estimated from the light field.

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