UGAE：面向G-PCC压缩点云的统一几何与属性增强
UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds
AI总结：
提出UGAE框架，通过几何后增强、属性前增强和属性后增强三个组件，显著提升G-PCC压缩点云的重建质量，在多个基准上大幅超越现有方法。
摘要：
点云的有损压缩降低了存储和传输成本，但不可避免地导致几何结构和属性信息的不可逆失真。为解决这些问题，我们提出了统一几何与属性增强（UGAE）框架，该框架包含三个核心组件：几何后增强（PoGE）、属性前增强（PAE）和属性后增强（PoAE）。在PoGE中，使用基于Transformer的稀疏卷积U-Net，通过预测体素占用概率来高精度重建几何结构。在细化后的几何结构基础上，PAE引入了一种创新的增强几何引导重着色策略，该策略使用细节感知的K近邻（DA-KNN）方法实现精确重着色，并在属性压缩前有效保留高频细节。最后，在解码器端，PoAE使用带有加权均方误差（W-MSE）损失的属性残差预测网络，在保持低频区域保真度的同时增强高频区域的质量。UGAE在三个基准数据集（8iVFB、Owlii和MVUB）上显著优于现有方法。与最新的G-PCC测试模型（TMC13v29）相比，UGAE在D1指标下实现了平均9.98 dB的BD-PSNR增益和90.98%的BD比特率节省（针对几何），以及在Y分量上实现了3.67 dB的BD-PSNR提升和56.88%的BD比特率节省（针对属性）。此外，它还显著提高了感知质量。
英文摘要：
Lossy compression of point clouds reduces storage and transmission costs; however, it inevitably leads to irreversible distortion in geometry structure and attribute information. To address these issues, we propose a unified geometry and attribute enhancement (UGAE) framework, which consists of three core components: post-geometry enhancement (PoGE), pre-attribute enhancement (PAE), and post-attribute enhancement (PoAE). In PoGE, a Transformer-based sparse convolutional U-Net is used to reconstruct the geometry structure with high precision by predicting voxel occupancy probabilities. Building on the refined geometry structure, PAE introduces an innovative enhanced geometry-guided recoloring strategy, which uses a detail-aware K-Nearest Neighbors (DA-KNN) method to achieve accurate recoloring and effectively preserve high-frequency details before attribute compression. Finally, at the decoder side, PoAE uses an attribute residual prediction network with a weighted mean squared error (W-MSE) loss to enhance the quality of high-frequency regions while maintaining the fidelity of low-frequency regions. UGAE significantly outperformed existing methods on three benchmark datasets: 8iVFB, Owlii, and MVUB. Compared to the latest G-PCC test model (TMC13v29), UGAE achieved an average BD-PSNR gain of 9.98 dB and 90.98% BD-bitrate savings for geometry under the D1 metric, as well as a 3.67 dB BD-PSNR improvement with 56.88% BD-bitrate savings for attributes on the Y component. Additionally, it improved perceptual quality significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
