A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences

TL;DR

This paper introduces a no-reference point cloud quality assessment method using captured video sequences and a modified ResNet3D, effectively correlating video features with human quality perception.

Contribution

It presents a novel no-reference PCQA metric based on multi-view captured videos and deep learning, addressing the lack of reference data in quality assessment.

Findings

Outperforms most state-of-the-art full-reference and no-reference metrics

Utilizes multi-view video sequences to capture static and dynamic information

Validates effectiveness through extensive experiments

Abstract

Point cloud is one of the most widely used digital formats of 3D models, the visual quality of which is quite sensitive to distortions such as downsampling, noise, and compression. To tackle the challenge of point cloud quality assessment (PCQA) in scenarios where reference is not available, we propose a no-reference quality assessment metric for colored point cloud based on captured video sequences. Specifically, three video sequences are obtained by rotating the camera around the point cloud through three specific orbits. The video sequences not only contain the static views but also include the multi-frame temporal information, which greatly helps understand the human perception of the point clouds. Then we modify the ResNet3D as the feature extraction model to learn the correlation between the capture videos and corresponding subjective quality scores. The experimental results show that our method outperforms most of the state-of-the-art full-reference and no-reference PCQA metrics, which validates the effectiveness of the proposed method.