计算机科学
点云
云计算
人工智能
失真(音乐)
水准点(测量)
利用
计算机视觉
投影(关系代数)
图形
数据挖掘
理论计算机科学
算法
放大器
计算机网络
计算机安全
大地测量学
带宽(计算)
地理
操作系统
作者
Weixin Xie,Kaimin Wang,Yakun Ju,Miaohui Wang
标识
DOI:10.1145/3581783.3611998
摘要
With the increasing communication and storage of point cloud data, there is an urgent need for an effective objective method to measure the quality before and after processing. To address this difficulty, we propose a projection-based blind quality indicator via multimodal learning for point cloud data, which can perceive both geometric distortion and texture distortion by using four homogeneous modalities (i.e., texture, normal, depth and roughness). To fully exploit the multimodal information, we further develop a deformable convolutionbased alignment module and a graph-based feature fusion module, and investigate a graph node attention-based evaluation method to forecast the quality score. Extensive experimental results on three benchmark databases show that our method achieves more accurate evaluation performance in comparison with 12 competitive methods.
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