用2D神经网络优化压缩点云颜色，提升视觉质量。
Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization
- 用轻量级Unet优化V-PCC生成的2D属性图
- 在8iVSLF数据集上显著改善压缩后点云颜色质量
- 结合迁移学习缓解点云训练数据不足问题
基于视频的点云压缩（V-PCC）通过传统视频编码器将动态点云数据转为视频序列以实现高效编码。然而，这种有损压缩会引入伪影，导致颜色属性退化。本文提出一种框架，旨在增强V-PCC压缩点云的颜色质量。我们设计了轻量级解压缩Unet（LDC-Unet），一种2D神经网络，用于优化V-PCC编码过程中生成的投影图。优化后的2D映射再反向投影回3D空间，以提升对应点云属性。此外，我们引入迁移学习策略，并构建了一个定制化的自然图像数据集进行初始训练，随后使用压缩点云的投影图进行微调。该策略有效缓解了点云训练数据稀缺的问题。实验在公开的8i voxelized full bodies long sequences（8iVSLF）数据集上进行，结果表明所提方法能显著提升颜色质量。
原文摘要 · Abstract (English)
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.
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