Multi-Frame Fusion Failure

Issue: Information from Multiple Frames/Views Not Properly Combined; Fusion Produces Worse Results Than Single Frame

Frequency: Common

Symptoms

• Fusion architecture fails to benefit from multiple views
• Single best frame often more accurate than fused result
• Conflicting information not resolved
• Information loss during fusion

Root Cause Fusing information from multiple sources is hard; requires alignment and conflict resolution. Naive concatenation or averaging can hurt performance if frames are misaligned or contain contradictory information. Fusion requires learned weighting or attention mechanisms.

Example

Scenario: 3D reconstruction from multi-view images
Image 1: Good front view
Image 2: Side view (slightly misaligned)
Image 3: Top view (very noisy)

Naive fusion: Average 3 views → mediocre 3D result
Smart fusion: Weight views by confidence; handle misalignment
Impact: Single-view sometimes better than fused

Key Statistics

• Single best frame accuracy: 85%
• Naive fusion (average): 75%
• Smart fusion (learned weights): 92%

Mitigation Strategies

1. Learned Fusion: Train attention/weighting module to combine views
2. Alignment First: Align frames before fusion
3. Soft Voting: Weighted voting based on confidence
4. Selective Fusion: Use only high-quality frames; skip noisy ones

Metrics

• Fusion gain (fused > single best frame)
• Weight distribution (is model using all frames?)

Alerts

• Fusion performance <single best frame → Debug

References

• Multi-View Fusion in 3D Vision
• Attention-based Fusion