Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization
This work addresses color artifacts in compressed point clouds for applications like virtual reality, but it is incremental as it builds on existing V-PCC methods.
The paper tackles color degradation in V-PCC compressed point clouds by proposing a lightweight 2D neural network (LDC-Unet) to optimize projection maps, resulting in improved color quality as demonstrated on the 8iVSLF dataset.
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.