A 3D Haar Wavelet Transform for Point Cloud Attribute Compression Based on Local Surface Analysis

Abstract

Point cloud is a main representation of 3D scenes. It is widely applied in many fields including autonomous driving, heritage reconstruction, virtual reality and augmented reality. The data size of this type of media is massive since it contains numerous points with each associated with a large amount of information including geometric coordinate, color, reflectance, and normal. It is thus of great significance to investigate the compression of point cloud data to boost its application. However, developing efficient point cloud compression method is challenging mainly due to the unstructured nature and nonuniform distribution of the data. In this paper, we propose a novel point cloud attribute compression algorithm based on Haar Wavelet Transform (HWT). More specifically, the transform is performed taking into account the surface orientation of point cloud. Experimental results demonstrate that the proposed method outperforms other state-of-the-art transforms.