Aggressive saliency-aware point cloud compression

TL;DR

This paper introduces a geometry-based, saliency-aware point cloud compression method that significantly improves quality at very low bit rates by prioritizing important regions based on geometry and user focus.

Contribution

It presents a novel end-to-end compression scheme that incorporates saliency maps derived from geometry and user position, optimizing data allocation for aggressive compression.

Findings

Outperforms MPEG G-PCC at small bit rates

Uses saliency maps to prioritize important points

Achieves better reconstruction quality with less data

Abstract

The increasing demand for accurate representations of 3D scenes, combined with immersive technologies has led point clouds to extensive popularity. However, quality point clouds require a large amount of data and therefore the need for compression methods is imperative. In this paper, we present a novel, geometry-based, end-to-end compression scheme, that combines information on the geometrical features of the point cloud and the user's position, achieving remarkable results for aggressive compression schemes demanding very small bit rates. After separating visible and non-visible points, four saliency maps are calculated, utilizing the point cloud's geometry and distance from the user, the visibility information, and the user's focus point. A combination of these maps results in a final saliency map, indicating the overall significance of each point and therefore quantizing different regions with a different number of bits during the encoding process. The decoder reconstructs the point cloud making use of delta coordinates and solving a sparse linear system. Evaluation studies and comparisons with the geometry-based point cloud compression (G-PCC) algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety of point clouds, demonstrate that the proposed method achieves significantly better results for small bit rates.