Compression Of Dynamic 3d Point Clouds Using Subdivisional Meshes And Graph Wavelet Transforms

The advent of advanced acquisition techniques in 3D media applications has led to an increasing trend of capturing dynamic objects and scenes via 3D point cloud sequences. This form of data is composed of time-indexed frames, each consisting of a collection of points with position and color attributes. Compression of such datasets is challenging because of the lack of efficient techniques for exploiting spatial and temporal correlations between the attributes. In our approach, we create an intermediate high-resolution representation of the point clouds, using consistent subdivisional triangular meshes, that captures all the features of the underlying object or scene. This representation is easy to obtain, significantly simplifies motion compensation and allows us to design efficient wavelet transforms using the recently developed framework of Biorthogonal Graph Wavelet Filterbanks. Preliminary experiments show that our approach can be an effective compression technique for 3D point cloud sequences.