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.