Depth Anything

Depth Anything research focuses on developing robust and efficient methods for depth estimation from images, aiming for improved accuracy and generalization across diverse scenes and modalities, including endoscopic and 360° imagery. Current research emphasizes foundation models, often leveraging transformer architectures and techniques like low-rank adaptation, to achieve zero-shot or few-shot performance on various datasets. This work is significant for advancing applications in robotics, autonomous navigation, 3D reconstruction, and medical imaging, where accurate and efficient depth perception is crucial for tasks ranging from object manipulation to surgical assistance. The integration of depth uncertainty estimation further enhances the reliability and applicability of these methods.