Frame Fusion

Frame fusion encompasses techniques that integrate information from multiple frames or data sources to enhance the quality, accuracy, or efficiency of various tasks. Current research focuses on developing novel architectures, such as transformers and U-Nets, and algorithms, including iterative optimization and attention mechanisms, to effectively fuse data across different modalities (e.g., images, LiDAR point clouds, events) and temporal scales. These advancements are significantly impacting fields like video stabilization, 3D object detection, and high dynamic range imaging by improving robustness, accuracy, and computational efficiency. The resulting improvements have broad implications for applications in autonomous driving, video processing, and computer vision.