Zoedepth Nyu Kitti
ZoeDepth is a depth estimation model fine-tuned on NYU and KITTI datasets, capable of estimating depth values in actual metric units.
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Release Time : 4/30/2024
Model Overview
This model extends the DPT framework for metric (absolute) depth estimation, excelling in zero-shot monocular depth estimation tasks.
Model Features
Metric depth estimation
Capable of estimating depth values in actual metric units (e.g., meters), not just relative depth.
Zero-shot transfer capability
Combines relative and metric depth estimation methods, demonstrating strong zero-shot transfer performance.
State-of-the-art performance
Achieves state-of-the-art performance levels in depth estimation tasks.
Model Capabilities
Monocular depth estimation
Zero-shot transfer learning
Metric depth prediction
Use Cases
Computer vision
3D scene reconstruction
Estimates scene depth information from a single image for 3D reconstruction.
Generates precise depth maps.
Autonomous driving perception
Used for environmental perception and obstacle detection in autonomous driving systems.
Provides accurate depth information to aid navigation decisions.
Augmented reality
Supplies scene depth information for AR applications.
Enables more realistic virtual object overlays.
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