3DTA: No-Reference 3D Point Cloud Quality Assessment With Twin Attention
Linxia Zhu, Jun Cheng, Xu Wang, Honglei Su, Huan Yang, Hui Yuan, Jari Korhonen
Abstract
Point clouds are rapidly gaining popularity in many practical applications, and point cloud quality assessment (PCQA) is an important research topic that helps us measure and improve the visual experience in applications using point clouds. Research on full-reference (FR) PCQAs has recently made impressive progress, and research on no-reference (NR) PCQAs has also gradually increased. However, the performance of the prior NR PCQA methods still suffers from weak generalization ability and lower accuracy than the FR metrics in general. In this work, we propose a two-stage sampling method that can reasonably represent a whole point cloud, making it possible to efficiently calculate the point cloud quality. For quality prediction, we designed a twin-attention-based transformer PCQA model (3DTA), which uses the data of the two-stage sampling method as input and directly outputs the predicted quality score. Our model is accurate and widely applicable, and it has a simple and flexible structure. Experimental results show that in most cases, the proposed 3DTA model substantially outperforms the benchmark NR methods. The accuracy of the proposed method is competitive even against that of the FR method, which makes 3DTA a strong candidate for the PCQA task, regardless of the reference availability. The code of the proposed model is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/philox12358/3DTA-PCQA</uri> .