Multiview point cloud filtering for spatiotemporal consistency

This work presents algorithms to resample and filter point cloud data reconstructed from multiple cameras and multiple time instants. In an initial resampling stage, a voxel or a surface mesh based approach resamples the point cloud data into a common sampling grid. Subsequently, the resampled data undergoes a filtering stage based on clustering to remove artifacts and achieve spatiotemporal consistency across cameras and time instants. The presented algorithms are evaluated in a view synthesis scenario. Results show that view synthesis with enhanced depth maps as produced by the algorithms leads to less artifacts than synthesis with the original source data.