Scale Aware Depth

Scale-aware depth estimation aims to accurately predict the distance of objects in images, overcoming the inherent scale ambiguity of monocular vision. Current research focuses on improving scale consistency across multiple views (e.g., using full surround monodepth or multi-view geometry), incorporating sparse depth priors from other sensors or geometric constraints, and developing robust model architectures like transformers and encoder-decoder networks to handle diverse scenes and improve detail generation. These advancements are crucial for applications like autonomous driving, robotic navigation, and medical imaging, enabling more reliable 3D scene understanding and improved performance in tasks requiring precise depth information.