Learning-based 3D point cloud quality assessment using a support vector regressor

Recent advances in capture technologies have increased the production of 3D content in the form of Point Clouds (PCs). The perceived quality of such data can be impacted by typical processing including acquisition, compression, transmission, visualization, etc. In this paper, we propose a learning-based method that efficiently predicts the quality of distorted PCs through a set of features extracted from the reference PC and its degraded version. The quality index is obtained here by combining the considered features using a Support Vector Regression (SVR) model. The performance contribution of each considered feature and their combination are compared. We then discuss the experimental results obtained in the context of state-of-the-art methods using 2 publicly available datasets. We also evaluate the ability of our method to predict unknown PCs through a cross-dataset evaluation. The results show the relevance of introducing a learning step to merge features for the quality assessment of such data. and the performance contribution of each is evaluated. We also evaluated quality prediction on through a cross-dataset evaluation. Our results show the relevance of introducing a learning step to merge features for PC quality assessment.