论文标题
G-PCC使用分数超分辨率的后处理
G-PCC Post-Processing Using Fractional Super-Resolution
论文作者
论文摘要
我们提出了一种通过将先前提出的分数超分辨率技术应用于用MPEG的G-PCC编解码器压缩和解码的云，提出了一种用于后处理点云的几何信息的方法。从某种意义上说，这是先前工作的延续，这仅需要一个缩小的点云和缩放因素，这两者都是由G-PCC编解码器提供的。对于非固定点云，需要先验缩放以提高效率。将该方法与GPCC本身以及基于机器学习的技术进行比较。结果表明，与后一种技术相比，GPCC的质量相对于GPCC有了很大的提高，与后一种技术相当
We present a method for post-processing point clouds' geometric information by applying a previously proposed fractional super-resolution technique to clouds compressed and decoded with MPEG's G-PCC codec. In some sense, this is a continuation of that previous work, which requires only a down-scaled point cloud and a scaling factor, both of which are provided by the G-PCC codec. For non-solid point clouds, an a priori down-scaling is required for improved efficiency. The method is compared to the GPCC itself, as well as machine-learning-based techniques. Results show a great improvement in quality over GPCC and comparable performance to the latter techniques, with the
