Optimization of octree-based adaptive geometry quantization via up-sampling for G-PCC

To improve the reconstructed point cloud after adaptive geometry quantization in geometry-based point cloud compression, a least squares plane (LSP) projection-based up-sampling method and a quantization parameter (QP) decision method based on loss function are proposed. First, the LSP fitting is carried out to locate the interpolated point based on the nearest neighbors of the current node during decoding, enhancing both the subjective and objective quality of the reconstructed point cloud. Second, the QP decision for each node is based on the mean squared error between the original point cloud and the reconstructed point cloud. The experimental results show that the proposed methods achieve performance gains in terms of point-to-point and point-to-plane errors for geometry by 6.3% and 1.6%, respectively, and for attributes by 1.5%, 0.7%, and 0.5%. There also has been a significant improvement in subjective quality.

I. Introduction

With the rapid development of 3D acquisition technologies, the demand for point clouds is increased to facilitate more realistic visualization of objects and scenarios in virtual reality, augmented reality, medical imaging, automatic driving, digital cities, robotics, etc. [1][2].