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BRIDGE -- Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation

Created by
  • Haebom

Author

Dingning Liu, Haoyu Guo, Jingyi Zhou, Tong He

Outline

BRIDGE is a framework for single-camera depth estimation (MDE). It generates over 20 million realistic, geometrically accurate RGB images and corresponding ground truth depth information using an optimized depth-to-image (D2I) generation method using reinforcement learning (RL). Based on this data, a depth estimation model is trained using a hybrid supervised learning strategy that combines teacher pseudo-labels and ground truth depth information. BRIDGE achieves innovation in scale and domain diversity, outperforming existing state-of-the-art methods.

Takeaways, Limitations

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Takeaways:
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Building and leveraging over 20 million datasets through RL-based D2I generation.
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Improving the Performance of Depth Estimation Models Using Hybrid Supervised Learning Strategies
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Achieves superior performance compared to existing state-of-the-art models
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Improved detail capture capabilities in complex scenes
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Capable of learning general and robust depth features
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Limitations:
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No specific Limitations mentioned in the paper.
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