Multi-view cost functions are used in multi-view stereo (MVS) and multi-view learning algorithms to aggregate information from multiple views or data sources. The cost function typically aims to optimize the matching or reconstruction process across different views.
Key Components of Multi-view Cost Functions
- Photo-consistency: This measures the similarity of corresponding pixels or features across different views12.
- Spatial regularization: This enforces smoothness and coherence in the reconstructed 3D geometry or learned representations2.
- View aggregation: Costs from individual views are combined, often using weighted sums or other fusion strategies13.
Examples of Multi-view Cost Functions
- MVS Cost Aggregation:
Where is the aggregated cost for pixel at depth hypothesis , is the weight for view , and is the cost from view1. - Multi-view Tracking Cost:
This cost function compares the likelihood of measurements belonging to an object versus being false positives3. - Multi-view Learning Reconstruction Error:
Used in auto-encoders to minimize reconstruction error across multiple views4.
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