Learn to Adapt for Self-Supervised Monocular Depth Estimation.

Area of Science:

• Computer Vision
• Machine Learning
• Deep Learning

Background:

• Monocular depth estimation is crucial for environmental perception but suffers from dataset domain gaps.
• Existing domain adaptation methods struggle with generalization to unseen datasets.
• Meta-overfitting hinders the transferability of self-supervised monocular depth estimation models.

Purpose of the Study:

• To boost the transferability of self-supervised monocular depth estimation models.
• To mitigate meta-overfitting in monocular depth estimation.
• To develop a method that generalizes effectively to new, unseen datasets.

Main Methods:

• Utilized model-agnostic meta-learning (MAML) for universal initial parameters.
• Proposed an adversarial depth estimation task to extract domain-invariant representations.
• Introduced a cross-task depth consistency constraint to stabilize training and improve performance.

Main Results:

• The proposed method demonstrates rapid adaptation to new domains.
• Achieved comparable results to state-of-the-art methods with significantly less training time (0.5 epoch vs. 20 epochs).
• Experimental validation on four diverse datasets confirmed the method's effectiveness.

Conclusions:

• The meta-learning and adversarial approach significantly improves the generalization and transferability of monocular depth estimation models.
• The method effectively overcomes domain shift issues, enabling fast and robust adaptation.
• This work offers a promising direction for developing more versatile and efficient self-supervised depth estimation systems.