Depth estimation is the process of determining the distance of objects or points in a scene from a specific viewpoint, often using visual data like images or video. It plays a crucial role in applications requiring spatial understanding, such as robotics, augmented reality, and autonomous vehicles.
How It Works:
- Single-Image Depth Estimation: Uses deep learning models to infer depth from a single image by analyzing features like shading, texture, and perspective.
- Stereo Depth Estimation: Calculates depth by comparing two images taken from slightly different viewpoints (e.g., stereo cameras) to measure disparity between corresponding points.
- Sensor-Based Methods: Combines data from devices like LiDAR or depth sensors with visual input for higher accuracy.
Applications:
- Autonomous Vehicles: Helps detect obstacles and estimate distances for safe navigation.
- Augmented and Virtual Reality: Enables realistic rendering of 3D environments.
- Robotics: Assists robots in understanding their environment and interacting with objects.
- Medical Imaging: Used in techniques like 3D reconstruction from 2D images.
Why It Matters:
Depth estimation is fundamental for machines to perceive and interact with the physical world. Accurate depth information allows AI systems to make informed decisions in real-time scenarios, improving their reliability and functionality.
