[Submitted on 8 Oct 2026]
Title:MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision
View a PDF of the paper titled MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision, by Xinzhe Wang and 10 other authors
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Abstract: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 estimation, outperforming COLMAP, a widely used but computationally expensive reference for vision-only benchmarks. Motivated by the scarcity of long-horizon monocular benchmarks for large-scale indoor reconstruction, we further build a multimodal data-collection platform and use MIRAGE to establish accurate 6-DoF reference trajectories for a new vision-only benchmark. We benchmark recent monocular large-scale reconstruction methods on this dataset, providing a systematic evaluation of their performance in long-horizon indoor scenes.
Comments: Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.12568 [cs.CV]
(or arXiv:2610.12568v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.12568
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xinzhe Wang [view email] [v1] Thu, 8 Oct 2026 18:00:56 UTC (47,265 KB)
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