A Review of Vision-Based Depth Estimation: Current Methods and Future Directions

Depth estimation is a core task in computer vision with applications in autonomous driving, robotics, augmented reality, and 3D reconstruction. In recent years, deep learning has significantly advanced depth prediction, enabling both single-image and multi-image approaches to achieve impressive accuracy across diverse environments. This paper presents a comprehensive review of current deep learning-based depth estimation methods, categorized by input type: single-image and multi-image. We examine key architectural developments, representative models, and benchmark performance on widely used datasets, particularly KITTI and NYU Depth-v2. Despite notable progress, current methods face critical limitations in real-world deployment, including high computational cost and limited robustness to environmental variation. These issues are especially concerning for safety-critical applications where real-time inference and stability are essential. We conclude by highlighting open challenges and encouraging future research toward efficient, robust, and deployable depth estimation systems.