VIDAR: Visual-Inertial Dense Alignment and Reconstruction via a Geometric Foundation Model

Abstract:Monocular foundation models provide dense geometry but usually lack a stable metric scale. This paper presents VIDAR, a visual-inertial dense reconstruction framework that couples SVO+IMU odometry with Depth Anything 3. VIDAR uses the visual-inertial front end as a metric anchor: it provides camera poses, scale, and a consistent world frame for aligning dense foundation-model predictions across time. The foundation model then contributes detailed local geometry that is fused into a global reconstruction. We study both pose-conditioned DA3 and a decoupled alignment strategy. On EuRoC, pose injection reduces scale error to about 1\% and reaches 0.463 mean F@0.10; the decoupled hybrid improves this to 0.676 without ground-truth poses. Results on EuRoC and TUM RGB-D show that VIDAR is a practical route to metric dense monocular reconstruction.

Robotics (cs.RO)

arXiv:2607.17171 [cs.RO]

(or arXiv:2607.17171v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.17171 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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