🤖 AI Summary
This work addresses the lack of metric-scale consistency in monocular foundation models despite their ability to predict dense geometry. The authors propose a visual-inertial guided dense alignment strategy that integrates SVO+IMU visual-inertial odometry with Depth Anything V3 to achieve globally consistent, metric-scale dense monocular reconstruction without requiring ground-truth poses. By leveraging either pose-conditioned or decoupled alignment mechanisms, the method fuses local depth predictions with scale, pose, and world-frame information from an inertial front-end. Experiments on the EuRoC dataset demonstrate that pose injection reduces scale error to approximately 1% and achieves an F@0.10 score of 0.463, while the decoupled hybrid strategy further improves performance to 0.676. Additional evaluation on TUM RGB-D confirms the approach’s generalization capability.
📝 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.