MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision
Feed-forward 3D priors enable high-quality reconstruction from image streams but often lack reliable metric scale and suffer from long-term trajectory drift. In contrast, LiDAR--inertial estimation provides metrically accurate motion but lacks the image-space consistency needed for high-fidelity reconstruction. We propose $MIRAGE$, which anchors feed-forward visual geometry to LiDAR--IMU metric motion and regulates online pose corrections according to correspondence support and cross-modal agreement, jointly enabling accurate metric pose estimation and high-fidelity Gaussian reconstruction. Experiments on Oxford Spires, M2DGR and INS show that MIRAGE achieves accurate metric trajectory es...
| Authors | Xinzhe Wang, Changjian Jiang, Kaiwen Song et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 October 2026 |
| Last updated at source | 14 October 2026 |
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