Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Emil Dumic; Anamaria Bjelopera; Andreas Nüchter

doi:10.3390/s22010197

Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Sensors (Basel). 2021 Dec 28;22(1):197. doi: 10.3390/s22010197.

Authors

Emil Dumic¹, Anamaria Bjelopera², Andreas Nüchter³

Affiliations

¹ Department of Electrical Engineering, University North, 104. Brigade 3, 42000 Varaždin, Croatia.
² Department of Electrical Engineering and Computing, University of Dubrovnik, Cira Carica 4, 20000 Dubrovnik, Croatia.
³ Department of Informatics VII-Robotics and Telematics, Julius-Maximilians-University Würzburg, 97074 Würzburg, Germany.

Abstract

In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.

Keywords: 3DTK toolkit; Poisson surface reconstruction; map projections; octree; point cloud compression; point-to-plane measure; point-to-point measure.