Stereo Attention

Stereo attention mechanisms are being actively developed to improve the processing and analysis of stereo image data, primarily aiming to enhance accuracy and efficiency in tasks like image super-resolution, anomaly detection, and stereo matching. Current research focuses on incorporating attention into various neural network architectures, including transformers and autoencoders, often employing techniques like cross-attention across epipolar lines or multi-level attention to capture both local and global context within stereo image pairs. These advancements lead to improved performance in applications ranging from 3D scene reconstruction and compression to more robust and accurate quality assessment of stereo images.