Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric

• URL: http://arxiv.org/abs/2208.14085v3
• Date: Wed, 6 Dec 2023 08:26:50 GMT
• Title: Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric
• Authors: Zicheng Zhang, Wei Sun, Yucheng Zhu, Xiongkuo Min, Wei Wu, Ying Chen, and Guangtao Zhai
• Abstract summary: This paper explores the way of dealing with point cloud quality assessment (PCQA) tasks via video quality assessment (VQA) methods. We generate the captured videos by rotating the camera around the point clouds through several circular pathways. We extract both spatial and temporal quality-aware features from the selected key frames and the video clips through using trainable 2D-CNN and pre-trained 3D-CNN models.
• Score: 58.309735075960745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud is one of the most widely used digital representation formats for three-dimensional (3D) contents, the visual quality of which may suffer from noise and geometric shift distortions during the production procedure as well as compression and downsampling distortions during the transmission process. To tackle the challenge of point cloud quality assessment (PCQA), many PCQA methods have been proposed to evaluate the visual quality levels of point clouds by assessing the rendered static 2D projections. Although such projection-based PCQA methods achieve competitive performance with the assistance of mature image quality assessment (IQA) methods, they neglect that the 3D model is also perceived in a dynamic viewing manner, where the viewpoint is continually changed according to the feedback of the rendering device. Therefore, in this paper, we evaluate the point clouds from moving camera videos and explore the way of dealing with PCQA tasks via using video quality assessment (VQA) methods. First, we generate the captured videos by rotating the camera around the point clouds through several circular pathways. Then we extract both spatial and temporal quality-aware features from the selected key frames and the video clips through using trainable 2D-CNN and pre-trained 3D-CNN models respectively. Finally, the visual quality of point clouds is represented by the video quality values. The experimental results reveal that the proposed method is effective for predicting the visual quality levels of the point clouds and even competitive with full-reference (FR) PCQA methods. The ablation studies further verify the rationality of the proposed framework and confirm the contributions made by the quality-aware features extracted via the dynamic viewing manner. The code is available at https://github.com/zzc-1998/VQA_PC.

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