Global Scene

Global scene understanding in computer vision aims to comprehensively represent and interpret the content and context of images and videos, moving beyond simple object recognition to capture holistic scene properties. Current research focuses on developing models, such as Neural Radiance Fields (NeRFs) and transformer-based architectures, that leverage both local and global features, including semantic information and geometric relationships, to improve accuracy and efficiency in tasks like image matching, video generation, and 3D scene reconstruction. This research is crucial for advancing applications in robotics, augmented reality, and autonomous systems, where robust and detailed scene understanding is essential for effective operation. The development of large, diverse datasets, like the LaRS maritime dataset, is also a key area, enabling more comprehensive and robust model training and evaluation.