Robust filtering of noisy scattered point data

Schall, Oliver; Belyaev, Alexander; Seidel, Hans‐Peter; Pauly, Mark; Zwicker, Matthias

doi:10.1109/pbg.2005.194067

Cited by 110 publications

(62 citation statements)

References 25 publications

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“…A first method shown in Point Cloud Library (2011) involves trimming points that are located further than a specified threshold, the interval between the mean and the standard deviation of the global distance, according to a Gaussian distribution of the distance. Other methods (Schall, Belyaev & Seidel, 2005) involve heavy calculations and, although they might produce a better result, the filtered point cloud may not significantly improve visualization.…”

Section: Point Cloud Visualization and Program Parallelizationmentioning

confidence: 99%

A method for creating interactive, user-resembling avatars

Silva

Moioli²

2017

PeerJ Computer Science

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Virtual reality (VR) applications have disseminated throughout several fields, with a special quest for immersion. The avatar is one of the key constituents of immersive applications, and avatar resemblance can provoke diverse emotional responses from the user. Yet a lot a virtual reality systems struggle to implement real life-like avatars. In this work, we propose a novel method for creating interactive, user-resembling avatars using available commercial hardware and software. Avatar visualization is possible with a point-cloud or a contiguous polygon surface, and avatar interactions with the virtual scenario happens through a body joint-approximation for contact. In addition, the implementation could be easily extended to other systems and its modular architecture admits improvement both on visualization and physical interactions. The code is under Apache License 2.0 and is freely available as Supplemental Information 1 in this article.

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Section: Point Cloud Visualization and Program Parallelizationmentioning

confidence: 99%

A method for creating interactive, user-resembling avatars

Silva

Moioli²

2017

PeerJ Computer Science

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“…Using noise removal techniques to identify areas of noise-like characteristics can be beneficial to determining the location of the defect. Schall et al propose a noise removal method that also provides applications in outlier removal (Schall et al 2005). The statistical method estimates the density of each area of the point cloud, and uses the neighbouring points to adapt a probable surface to each point.…”

Section: Surface Shape Analysismentioning

confidence: 99%

3D Surface Analysis for Automated Detection of Deformations on Automotive Body Panels

Yogeswaran¹,

Payeur²

2012

New Advances in Vehicular Technology and Automotive Engineering

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“…A smooth kernel density function g(p) is defined to reflect the probability that a point pє R 3 is a point on the surface S sampled by a noisy point cloud Ω. Inspired by the previous work of Schall et al [21], we measure the probability density function g(p) by considering the squared distance of p to the plane H(p) fitted to a spatial k-neighborhood of p i as…”

Section: Mean Shift Filteringmentioning

confidence: 99%

Implicit Surface Reconstruction from Scattered Point Data with Noise

Yang

Wang

Zhu

et al. 2007

Computational Science – ICCS 2007

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Abstract. This paper addresses the problem of reconstructing implicit function from point clouds with noise and outliers acquired with 3D scanners. We introduce a filtering operator based on mean shift scheme, which shift each point to local maximum of kernel density function, resulting in suppression of noise with different amplitudes and removal of outliers. The "clean" data points are then divided into subdomains using an adaptive octree subdivision method, and a local radial basis function is constructed at each octree leaf cell. Finally, we blend these local shape functions together with partition of unity to approximate the entire global domain. Numerical experiments demonstrate robust and high quality performance of the proposed method in processing a great variety of 3D reconstruction from point clouds containing noise and outliers.

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Robust filtering of noisy scattered point data

Abstract: Abstract

Cited by 110 publications

References 25 publications

A method for creating interactive, user-resembling avatars

A method for creating interactive, user-resembling avatars

3D Surface Analysis for Automated Detection of Deformations on Automotive Body Panels

Implicit Surface Reconstruction from Scattered Point Data with Noise

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