Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization

TL;DR

This paper presents a novel neural network-based framework to enhance color quality in V-PCC compressed point clouds by optimizing 2D attribute maps, addressing compression artifacts effectively.

Contribution

It introduces LDC-Unet, a lightweight neural network for optimizing projection maps in V-PCC, along with a transfer learning strategy and a new dataset for training.

Findings

Significant improvement in color quality of compressed point clouds

Effective use of transfer learning with a custom dataset

Validated on public dataset showing enhanced visual results

Abstract

Video-based point cloud compression (V-PCC) converts the dynamic point cloud data into video sequences using traditional video codecs for efficient encoding. However, this lossy compression scheme introduces artifacts that degrade the color attributes of the data. This paper introduces a framework designed to enhance the color quality in the V-PCC compressed point clouds. We propose the lightweight de-compression Unet (LDC-Unet), a 2D neural network, to optimize the projection maps generated during V-PCC encoding. The optimized 2D maps will then be back-projected to the 3D space to enhance the corresponding point cloud attributes. Additionally, we introduce a transfer learning strategy and develop a customized natural image dataset for the initial training. The model was then fine-tuned using the projection maps of the compressed point clouds. The whole strategy effectively addresses the scarcity of point cloud training data. Our experiments, conducted on the public 8i voxelized full bodies long sequences (8iVSLF) dataset, demonstrate the effectiveness of our proposed method in improving the color quality.