Point clouds are utilized in various 3D applications such as cross-reality (XR) and realistic 3D displays. In some applications, e.g., for live streaming using a 3D point cloud, real-time point cloud denoising methods are required to enhance the visual quality. However, conventional high-precision denoising methods cannot be executed in real time for large-scale point clouds owing to the complexity of graph constructions with K nearest neighbors and noise level estimation. This paper proposes a fast graph-based denoising (FGBD) for a large-scale point cloud. First, high-speed graph construction is achieved by scanning a point cloud in various directions and searching adjacent neighborhoods on the scanning lines. Second, we propose a fast noise level estimation method using eigenvalues of the covariance matrix on a graph. Finally, we also propose a new low-cost filter selection method to enhance denoising accuracy to compensate for the degradation caused by the acceleration algorithms. In our experiments, we succeeded in reducing the processing time dramatically while maintaining accuracy relative to conventional denoising methods. Denoising was performed at 30fps, with frames containing approximately 1 million points.

翻译：点云被用于各种三维应用，如跨现实（XR）和逼真三维显示。在某些应用（例如使用三维点云的直播流）中，需要实时点云去噪方法来增强视觉质量。然而，由于基于 K 近邻的图构建和噪声水平估计的复杂性，传统高精度去噪方法无法在大规模点云上实时执行。本文提出了一种用于大规模点云的快速基于图去噪（FGBD）方法。首先，通过在不同方向扫描点云并在扫描线上搜索相邻邻域，实现了高速图构建。其次，我们提出了一种利用图上协方差矩阵特征值的快速噪声水平估计方法。最后，我们还提出了一种新的低成本滤波器选择方法，以提高去噪精度，补偿加速算法导致的性能下降。在我们的实验中，与传统的去噪方法相比，我们在保持精度的同时成功大幅减少了处理时间。去噪处理以每秒30帧的速度执行，每帧包含约100万个点。