  Point clouds are a general format for representing realistic 3D objects in
diverse 3D applications. Since point clouds have large data sizes, developing
efficient point cloud compression methods is crucial. However, excessive
compression leads to various distortions, which deteriorates the point cloud
quality perceived by end users. Thus, establishing reliable point cloud quality
assessment (PCQA) methods is essential as a benchmark to develop efficient
compression methods. This paper presents an accurate full-reference point cloud
quality assessment (FR-PCQA) method called full-reference quality assessment
using support vector regression (FRSVR) for various types of degradations such
as compression distortion, Gaussian noise, and down-sampling. The proposed
method demonstrates accurate PCQA by integrating five FR-based metrics covering
various types of errors (e.g., considering geometric distortion, color
distortion, and point count) using support vector regression (SVR). Moreover,
the proposed method achieves a superior trade-off between accuracy and
calculation speed because it includes only the calculation of these five simple
metrics and SVR, which can perform fast prediction. Experimental results with
three types of open datasets show that the proposed method is more accurate
than conventional FR-PCQA methods. In addition, the proposed method is faster
than state-of-the-art methods that utilize complicated features such as
curvature and multi-scale features. Thus, the proposed method provides
excellent performance in terms of the accuracy of PCQA and processing speed.
Our method is available from https://github.com/STAC-USC/FRSVR-PCQA.
