Unsupervised Domain Adaptation for Depth Prediction from Images.

Area of Science:

• Computer Vision
• Machine Learning
• Deep Learning

Background:

• State-of-the-art dense depth estimation uses CNNs trained on large datasets.
• These methods degrade significantly due to domain shift, where training and target environments differ.
• Current solutions involve fine-tuning with costly depth labels, which are often impractical.

Purpose of the Study:

• To develop an unsupervised domain adaptation technique for depth estimation.
• To address the domain shift problem without requiring groundtruth depth labels.
• To improve the robustness of depth prediction architectures in novel environments.

Main Methods:

• Leveraging classical stereo algorithms to generate disparity measurements and confidence scores from image pairs.
• Proposing a novel confidence-guided loss function for fine-tuning depth prediction models.
• Adapting both depth-from-stereo and depth-from-monocular architectures.

Main Results:

• The proposed unsupervised method effectively mitigates the domain shift issue.
• The technique demonstrates strong performance on standard datasets and evaluation protocols.
• Outperforms existing state-of-the-art unsupervised loss functions for domain adaptation.

Conclusions:

• Unsupervised domain adaptation using confidence-guided loss is a viable solution for robust depth estimation.
• The method significantly improves depth prediction accuracy in out-of-distribution environments.
• Eliminates the need for expensive groundtruth depth data in target domains.