Point Cloud Quality

Point cloud quality assessment (PCQA) focuses on developing objective metrics to evaluate the perceptual quality of 3D point cloud data, crucial for various applications like autonomous driving and 3D modeling. Current research emphasizes both full-reference (FR-PCQA) and no-reference (NR-PCQA) methods, employing deep learning architectures such as transformers, convolutional neural networks, and graph neural networks to analyze geometric and textural features, often incorporating insights from human visual perception. Accurate PCQA is vital for optimizing 3D data compression, processing, and rendering techniques, ultimately improving the quality and reliability of applications relying on point cloud data.