FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry
- Published
- Source
- arXiv
- Paper number
- 614
- Field
- Computer Vision
- arXiv ID
- 2607.11588
Key points
- Stage 1 uses DINOv3 initialization and a 10.2M multi-domain corpus to learn affine-invariant, high-quality relative geometry.
- Stage 2 applies a pixel-wise scale field and ray-direction correction field for spatially varying metric correction.
- We identify focal-length distribution mismatch as the key bottleneck in zero-shot metric generalization.
- By augmenting with Blender-based synthetic data over a wider range of focal lengths, we improve robustness to intrinsics shift.
- Across seven zero-shot benchmarks, it leads by an average of more than 5.2%, preventing large drops across domains.
- It outperforms existing metric methods such as DepthPro, Metric3D V2, UniDepthV2, and MoGe-2.
Paper links
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