Multi-frame depth estimation is the depth prediction strategy that fuses temporal evidence from multiple frames to improve metric stability and detail beyond single-image depth - it combines learned priors with explicit motion-based cues.

What Is Multi-Frame Depth Estimation?

• Definition: Estimate depth for a target frame using neighboring frames and temporal correspondences.
• Key Signal: Parallax and temporal consistency reduce ambiguity in monocular cues.
• Architectures: Cost-volume fusion, recurrent depth networks, and transformer temporal aggregators.
• Output Goal: More accurate and stable depth maps over time.

Why Multi-Frame Depth Matters

• Metric Accuracy: Temporal geometry helps resolve scale and structure ambiguities.
• Temporal Stability: Reduces frame-to-frame depth flicker.
• Robustness: Better performance in low-texture or ambiguous scenes.
• Task Performance: Improves downstream navigation and 3D reconstruction.
• Hybrid Value: Bridges monocular priors with geometric measurement signals.

Modeling Strategies

Cost Volume Construction:

• Compare target features with warped source features at candidate depths.
• Select depth with strongest matching evidence.

Temporal Fusion Networks:

• Aggregate depth cues recurrently across short clips.
• Improve consistency and noise resistance.

Confidence-Aware Blending:

• Weight monocular prior versus temporal evidence by reliability.
• Prevents overconfidence under weak motion.

How It Works

Step 1:

• Build temporal correspondences from adjacent frames and extract multi-frame features.

Step 2:

• Fuse cues into depth prediction network and refine output with temporal consistency constraints.

Multi-frame depth estimation is a high-accuracy depth strategy that leverages temporal parallax to outperform single-frame inference in dynamic real scenes - it is especially effective when camera motion provides rich geometric cues.