Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network

In recent years, 3D point clouds have experienced rapid growth in various fields of computer vision, increasing the demand for efficient approaches to automatically assess the quality of 3D point clouds. In this paper, we propose a blind point cloud quality assessment method based on deep learning, that takes an input point cloud object and predicts its quality score. The proposed approach starts with projecting each 3D point cloud into rendering views (2D images). It then feeds these views to a deep convolutional neural network (CNN) to obtain the perceptual quality scores. In order to predict accurately the quality score, we use transfer learning to exploit the high potential of VGG-16, which is a classification model trained on ImageNet database. We evaluate the performance of our model on two benchmark databases: ICIP2020 and SJTU. Based on the results analysis, our model shows a strong correlation between the predicted and the subjective quality scores, showing promising results, and outperforming the state-of-the-art point cloud quality assessment models.