This paper deals with the current trends of new compression methods for 3D point cloud contents required to ensure efficient transmission and storage.
The representation of 3D point clouds geometry remains a challenging problem, since this signal is unstructured.
In this paper, we introduce a new hierarchical geometry representation based on adaptive Tree-Structured Point-Lattice Vector Quantization (TSPLVQ).
This representation enables hierarchically structured 3D content that improves the compression performance for static point clouds.
The novelty of the proposed scheme lies in adaptive selection of the optimal quantization scheme of the geometric information, that better leverage the intrinsic correlations in point cloud.
Based on its adaptive and multiscale structure, two quantization schemes are dedicated to project recursively the 3D point clouds into a series of embedded truncated cubic lattices.
At each step of the process, the optimal quantization scheme is selected according to a rate-distortion cost in order to achieve the best tradeoff between coding rate and geometry distortion, such that the compression flexibility and performance can be greatly improved.
Experimental results show the interest of the proposed multi-scale method for lossy compression of geometry.

Index Terms: 3D point cloud geometry, lattice vector quantization, rate-distortion optimization