Semi-Supervised Adversarial Monocular Depth Estimation.

Area of Science:

• Computer Vision
• Machine Learning
• Deep Learning

Background:

• State-of-the-art monocular depth estimation methods require extensive labeled image-depth pairs, which are costly and difficult to obtain.
• Existing approaches struggle with limited training data, hindering accurate depth regression.

Purpose of the Study:

• To develop a semi-supervised adversarial learning framework for monocular depth estimation that overcomes the limitations of scarce labeled data.
• To leverage a small number of image-depth pairs alongside numerous unlabeled monocular images to achieve high-performance depth prediction.

Main Methods:

• A novel semi-supervised adversarial learning framework employing one generator for depth regression and two discriminators for evaluation.
• Discriminators provide feedback on both image-depth pairs and depth channels to refine generator predictions.
• Utilizes a generator-discriminator architecture to enhance depth prediction realism and accuracy.

Main Results:

• The proposed approach improves state-of-the-art models on the NYUD v2 dataset by effectively utilizing unlabeled data.
• Achieves state-of-the-art accuracy on the Make3D dataset with a limited training set.
• Demonstrates strong adaptability to unseen datasets (Make3D) after training on annotated data (KITTI).

Conclusions:

• Semi-supervised adversarial learning offers a viable solution for monocular depth estimation with limited labeled data.
• The framework effectively enhances depth prediction accuracy and generalizability across different datasets.
• This method significantly reduces the dependency on large-scale, expensive annotated datasets for training depth estimation models.