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Building a Modular Computer Vision Perception System: Part 3 — Depth Estimation
- Depth estimation provides crucial spatial information for robots to navigate and interact with objects in a room.
- It adds the missing dimension of depth to traditional 2D computer vision, creating a 3D understanding.
- Monocular depth estimation uses neural networks to infer depth from 2D images accurately.
- Depth estimation helps in autonomous driving, robotics, augmented reality, and scene understanding applications.
- Implementing depth estimation from a single 2D image is challenging due to the loss of explicit depth info.
- The MiDaS model by Intel Labs is a robust choice for relative depth estimation.
- Abstract base classes defining interfaces and concrete implementations are crucial for depth estimation.
- Depth estimators can enhance object detection and tracking by providing spatial information.
- Depth maps help transform 2D image coordinates into richer 3D positions for scene understanding.
- Visualizing depth maps with warmer colors for closer objects and cooler colors for farther objects aids in interpretation.
- Optimizations and configurations are necessary for efficient depth estimation in various deployment environments.
- Understanding the limitations of monocular depth estimation is crucial for precise applications.
- Temporal smoothing and other extensions can further improve the depth estimation module.
- Monocular depth estimation balances capability and simplicity but may require supplemental sensors for precise metrics.
- The next article will delve into image segmentation to enhance pixel-level object identification.
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