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A Self-regulating Spatio-Temporal Filter for Volumetric Video Point Clouds
[chapter]
2020
Communications in Computer and Information Science
The following work presents a self-regulating filter that is capable of performing accurate upsampling of dynamic point cloud data sequences captured using wide-baseline multi-view camera setups. This is achieved by using two-way temporal projection of edge-aware upsampled point clouds while imposing coherence and noise filtering via a windowed, self-regulating noise filter. We use a state of the art Spatio-Temporal Edge-Aware scene flow estimation to accurately model the motion of points
doi:10.1007/978-3-030-41590-7_16
fatcat:uuozxj5qf5ev3cckmtqwii6xrq