BRIDGE - Building Reinforcement-Learning Depth-to-Image Data Generation
  Engine for Monocular Depth Estimation
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/.
68db59b1d2bf1f4b15ec75a2
Dingning Liu
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2025-09-30T06:00:38.759Z
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L
user
Dingning
68db59b1d2bf1f4b15ec75a3
Haoyu Guo
claimed_verified
2025-09-30T06:00:36.341Z
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Haoyu Guo
user
ghy0324
68db59b1d2bf1f4b15ec75a4
Jingyi Zhou
68db59b1d2bf1f4b15ec75a5
Tong He
BRIDGE - Building Reinforcement-Learning Depth-to-Image Data Generation
  Engine for Monocular Depth Estimation
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/.
BRIDGE uses RL-optimized depth-to-image generation to create a large, diverse dataset, enhancing monocular depth estimation robustness and performance.
monocular depth estimation RL-optimized depth-to-image ground truth depth hybrid supervision teacher pseudo-labels complex scene detail capture