Depth from video is the estimation of per-pixel scene distance by exploiting temporal parallax and multi-frame geometric consistency - motion between frames provides strong cues about relative and absolute depth under suitable camera movement.

What Is Depth from Video?

• Definition: Infer depth maps using monocular or multi-view video sequences.
• Key Cue: Parallax where closer points move more in image coordinates under camera motion.
• Model Types: Geometry-based SfM pipelines, self-supervised monocular depth networks, and hybrid systems.
• Output Use: 3D reconstruction, navigation, and AR scene understanding.

Why Depth from Video Matters

• 3D Awareness: Converts 2D video into metric scene structure.
• Sensor Savings: Enables depth estimation without dedicated depth hardware.
• Planning Support: Essential for obstacle avoidance and spatial reasoning.
• Rendering Utility: Depth improves compositing and view synthesis quality.
• Scalable Data: Can train from large unlabeled video corpora via photometric constraints.

Depth Estimation Strategies

Structure-from-Motion Geometry:

• Recover camera poses and triangulate points from feature matches.
• Produces sparse or semi-dense depth.

Self-Supervised Depth Nets:

• Predict depth and pose jointly with view synthesis losses.
• Works on monocular sequences at scale.

Hybrid Refinement:

• Fuse geometric priors with neural depth prediction.
• Improves robustness in low-texture regions.

How It Works

Step 1:

• Estimate inter-frame motion and correspondences from video.

Step 2:

• Solve depth through geometric triangulation or train depth model with temporal photometric consistency.

Depth from video is a core geometric inference task that turns temporal motion cues into actionable 3D scene understanding - reliable depth estimation enables richer perception and control in many vision systems.