Depth Analogy: Data-Driven Approach for Single Image Depth Estimation Using Gradient Samples.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Estimating scene depth from single monocular images is a fundamentally ill-posed problem.
• Existing data-driven methods often struggle with diverse scene characteristics and training data variations.

Purpose of the Study:

• To present a novel gradient-domain approach for monocular depth estimation using depth analogy.
• To improve depth recovery by leveraging depth gradients rather than direct depth values.

Main Methods:

• A non-parametric learning process is employed to synthesize target depth fields using analogous depth information.
• Depth gradients are sampled from training RGB-D image pairs via visual correspondence.
• Poisson reconstruction integrates the transferred depth gradients as reconstruction cues.

Main Results:

• The proposed gradient-domain approach demonstrates superior performance compared to existing depth-domain methods.
• The method shows robustness even with uncorrelated training datasets and significant variations in appearance and geometry.
• Depth gradient transfer proves less sensitive to scene characteristics than direct depth value transfer.

Conclusions:

• The depth analogy method offers a more robust and versatile solution for monocular depth estimation.
• Gradient-domain processing is key to overcoming the limitations of conventional data-driven depth recovery techniques.