Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, objective and subjective quality assessments of compressed 3D content remain open problems and an area of research interest. Yet most of the efforts in the research area ignore the local geometrical structures between points representation. In this paper, we overcome this limitation by introducing a novel and efficient objective metric for Point Clouds Quality Assessment, by learning local intrinsic dependencies using Graph Neural Network (GNN). To evaluate the performance of our method, two well-known datasets have been used. The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics.
Qingdao University（青岛大学）；New York University（纽约大学）；Shandong University（山东大学）；De Montfort University（德蒙福特大学）
With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages. However, the existing point cloud quality assessments ignore the mechanism of human visual system (HVS) which has an important impact on the accuracy of the perceptual quality assessment. In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed. The PKT…
Point cloud quality assessment (PCQA) has become an appealing research field in recent days. Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, called point cloud quality assessment using 3D saliency maps (PQSM). Specifically, we first propose a projection-based point cloud saliency map generation method, in which depth information is introduced to better reflect the geometric characteristics of point clouds. Then, we constr…
No-Reference Point Cloud Quality Assessment via Graph Convolutional Network
School of Information Science and Engineering, Ningbo University（宁波大学信息科学与工程学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（上海交通大学图像通信与信息处理研究所）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds
College of Electronics and Information, Qingdao University（青岛大学电子与信息学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peng Cheng Laboratory（鹏城实验室）；School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体创新中心）
We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the …
UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
School of Automation, Nanjing University of Science and Technology（自动化学院，南京理工大学）；National Key Laboratory of Information Systems Engineering（信息系统工程国家重点实验室）；School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）
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…
Point Cloud Compression and Objective Quality Assessment: A Survey
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学合作中位网创新中心）；University of Missouri–Kansas City（密苏里大学-堪萨斯城分校）；Department of Electrical and Computer Engineering, University of Canterbury（坎特伯雷大学电子与计算机工程系）
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
Cooperative Medianet Innovation Center, Shanghai Jiaotong University（上海交通大学合作Medianet创新中心）；University of Missouri-Kansas City（密苏里大学堪萨斯分校）；University of Canterbury（坎特伯雷大学）
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（上海交通大学图像通信与网络工程研究所）
To improve the viewer's Quality of Experience (QoE) and optimize computer graphics applications, 3D model quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most widely used digital representation formats of 3D models, the visual quality of which is quite sensitive to lossy operations like simplification and compression. Therefore, many related studies such as point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality degradations of 3D models. However, a large part of…
The Worse The Better: Content-Aware Viewpoint Generation Network for Projection-related Point Cloud Quality Assessment
School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity
School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；School of Telecommunication Engineering, Xidian University（电信工程学院，西安电子科技大学）
No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
