A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences
This addresses the challenge of evaluating point cloud quality without a reference, which is important for applications like 3D modeling and virtual reality, but it is incremental as it builds on existing video-based and deep learning approaches.
The paper tackles the problem of no-reference quality assessment for colored point clouds by using captured video sequences from rotating cameras, and it shows that the method outperforms most state-of-the-art metrics in experiments.
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.