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.