Monocular depth estimation is the prediction of dense depth maps from a single RGB image using geometric cues learned from data - despite no explicit stereo baseline at inference, models infer relative distance from perspective, texture, and semantic priors.

What Is Monocular Depth Estimation?

• Definition: Map each pixel to an estimated depth value from one camera frame.
• Inference Constraint: Single-image input without direct triangulation.
• Output Type: Relative depth or metric depth depending on training setup.
• Model Families: CNN encoders, transformer decoders, and hybrid geometry-aware networks.

Why Monocular Depth Matters

• Hardware Simplicity: Depth perception without dedicated depth sensors.
• Wide Applicability: Useful in AR, robotics, autonomous driving, and scene understanding.
• Data Availability: Can leverage large image datasets and self-supervised video training.
• Pipeline Foundation: Supports obstacle reasoning and 3D reconstruction tasks.
• Cost Efficiency: Enables scalable depth deployment on commodity cameras.

Depth Cues Used by Models

Perspective and Geometry:

• Vanishing points and converging lines imply depth structure.

Semantic Priors:

• Known object sizes and scene context guide distance estimation.

Texture and Blur Patterns:

• Gradient density and focus cues correlate with depth.

How It Works

Step 1:

• Encode RGB image into multi-scale feature hierarchy capturing local and global context.

Step 2:

• Decode features into dense depth map with scale-aware refinement and optional uncertainty prediction.

Monocular depth estimation is a high-impact perception capability that extracts 3D structure from ordinary camera imagery - strong models combine learned semantics with geometric consistency for reliable depth predictions.