Fast graph-based denoising for point cloud color information

TL;DR

This paper introduces a rapid graph-based denoising method for large-scale point clouds that achieves real-time processing at 30fps while maintaining high accuracy, suitable for live 3D applications.

Contribution

It presents a novel fast graph construction, noise level estimation, and filter selection techniques to enable real-time denoising of large point clouds.

Findings

Achieved 30fps denoising of 1 million point frames

Reduced processing time significantly compared to traditional methods

Maintained denoising accuracy comparable to high-precision approaches

Abstract

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.