Cross Scene

Cross-scene generalization, the ability of a model to perform well across diverse and unseen environments, is a central challenge in computer vision and robotics. Current research focuses on developing robust model architectures, such as neural radiance fields (NeRFs) and transformer-based networks, that can effectively leverage data from multiple scenes to improve generalization capabilities, often incorporating techniques like mixture-of-experts and attention mechanisms. These advancements are crucial for deploying AI systems in real-world scenarios where encountering novel environments is inevitable, impacting applications ranging from autonomous navigation to remote sensing and 3D scene understanding. The development of new datasets and evaluation metrics further supports the rigorous assessment and improvement of cross-scene generalization performance.