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OUTLIERS REMOVAL & CONSOLIDATION OF DYNAMIC POINT CLOUD

Citation Author(s):
Gerasimos Arvanitis, Aris Spathis-Papadiotis, Aris S. Lalos, Konstantinos Moustakas, Nikos Fakotakis
Submitted by:
Gerasimos Arvanitis
Last updated:
5 October 2018 - 3:54am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Gerasimos Arvanitis
Paper Code:
2509

Abstract 

Abstract: 

Recently, there has been increasing interest in the processing of
dynamic scenes as captured by 3D scanners, ideally suited for
challenging applications such as immersive tele-presence systems
and gaming. Despite the fact that the resolution and accuracy of
the modern 3D scanners are constantly improving, the captured
3D point clouds are usually noisy with a perceptive percentage of
outliers, stressing the need of an approach with low computational
requirements which will be able to automatically remove the outliers
and create a consolidated point cloud.
In this paper, we introduce a novel method which first recognizes
and removes outliers from a dynamic point cloud sequence
(DPCS) using a very fast Robust PCA (RPCA) approach and then
we use a novel weighted Laplacian interpolation approach to achieve
a fast and effective consolidation of a DPCS. Extensive evaluation
studies, carried out using a collection of different DPCS, verify
that the proposed technique achieves plausible reconstruction output
despite the constraints posed by arbitrarily complex motion
scenarios.

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OUTLIERS REMOVAL & CONSOLIDATION OF DYNAMIC POINT CLOUD

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