用强化学习生成2000万张带真实深度的图像，提升单目深度估计性能
BRIDGE -- Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation
- 通过强化学习从多源深度图生成2000万张逼真图像及对应深度
- 在复杂场景中实现比现有方法更优的深度估计精度和细节捕捉
- 适合需要高精度深度估计的自动驾驶、机器人领域研究者
单目深度估计是计算机视觉的基础任务。传统方法受限于数据稀缺与质量不足，影响其鲁棒性。为此，我们提出BRIDGE，一种基于强化学习优化的深度到图像（D2I）生成框架，从多样化的源深度图中合成超过2000万张真实且几何准确的RGB图像，每张图像均配以真实深度标签。随后，我们在此数据集上训练深度估计模型，采用融合教师伪标签与真实深度的混合监督策略，实现全面而稳健的训练。该创新的数据生成与训练范式在规模与域多样性上取得突破，定量结果及复杂场景细节捕捉能力持续优于现有最先进方法，有效促进通用且鲁棒的深度特征学习。代码与模型见https://dingning-liu.github.io/bridge.github.io/。
原文摘要 · Abstract (English)
Monocular Depth Estimation (MDE) is a foundational task for computer vision. Traditional methods are limited by data scarcity and quality, hindering their robustness. To overcome this, we propose BRIDGE, an RL-optimized depth-to-image (D2I) generation framework that synthesizes over 20M realistic and geometrically accurate RGB images, each intrinsically paired with its ground truth depth, from diverse source depth maps. Then we train our depth estimation model on this dataset, employing a hybrid supervision strategy that integrates teacher pseudo-labels with ground truth depth for comprehensive and robust training. This innovative data generation and training paradigm enables BRIDGE to achieve breakthroughs in scale and domain diversity, consistently outperforming existing state-of-the-art approaches quantitatively and in complex scene detail capture, thereby fostering general and robust depth features. Code and models are available at https://dingning-liu.github.io/bridge.github.io/.
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