Systems and methods for generating dimensionally coherent training data
By transforming 2D images into 3D visualizations and perturbing 3D representations, the method generates dimensionally coherent training data that addresses inaccuracies in feature matching, improving network performance and reducing computational costs.
Patent Information
- Authority / Receiving Office
- AU · AU
- Patent Type
- Applications
- Current Assignee / Owner
- HOVER INC
- Filing Date
- 2023-02-23
- Publication Date
- 2026-07-23
AI Technical Summary
Existing 3-D reconstruction methods generate training data that is not dimensionally coherent, leading to inaccurate feature matching and increased computational costs due to the use of 2D data formats that fail to preserve 3D relationships and introduce variability, resulting in false positives and negatives when deployed in real-world scenarios.
Transform 2D images into 3D visualizations that preserve spatial relationships and generate synthetic views by perturbing 3D representations, using visibility masks and photo-texturing to create training datasets that maintain accurate 3D geometries and relationships.
Improves the accuracy of feature matching by training networks on spatially coherent data, reducing computational costs and enhancing the network's ability to recognize features across different camera views.
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