Single-View Depth Estimation: Advancing 3D Scene Interpretation With One Lens

This paper introduces an advanced technique for monocular 3D scene understanding, utilizing deep learning to estimate depth from a single image. Traditional methods, such as stereo vision systems or hardware-based approaches like LiDAR, require multiple viewpoints or specialized equipment to calculate depth, making them complex and costly. In contrast, this method leverages convolutional neural networks (CNNs) trained on large-scale datasets containing ground truth depth information to predict the relative depth of objects in a scene and visualize it in a 3D perspective. By recognizing visual cues such as perspective, shading, and occlusion, the model effectively captures spatial relationships from a single viewpoint, offering a streamlined solution for depth estimation. The approach is evaluated on benchmark datasets like KITTI and NYU Depth V2, which cover a wide range of environments, including urban streets and indoor settings. The results demonstrate that this single-lens depth estimation method achieves accuracy comparable to traditional stereo systems, without the need for additional hardware. Its versatility makes it particularly valuable for real-world applications like autonomous navigation, where accurate depth perception is essential for tasks such as obstacle avoidance and path planning, and augmented reality, where it enhances the interaction between virtual and physical objects. Furthermore, its computational efficiency allows for real-time depth estimation, making it suitable for time-critical applications in fields such as robotics and drones. Overall, this paper highlights the potential of single-lens depth perception as a scalable, cost-effective alternative to conventional 3D scene understanding techniques, with promising implications for industries relying on depth sensing technology.