FoundationVIO: Enforcing Metric Scale in Feed-forward Visual-Inertial Odometry
Motivated by parallels with classical visual-inertial odometry, we introduce FoundationVIO which integrates inertial sensing with GFM predictions to recover metric scale reconstruction using a factor-graph based approach.
ProofPaper ↗
Key points
- Feed-forward geometric foundation models (GFMs) directly predict dense geometry and camera poses from batches of images.
- Emerging visual SLAM methods chain together consecutive GFM batches to form dense point cloud maps.
- A core challenge is how to address the ambiguous geometric scale of GFM predictions: independent predictions are locally consistent up-to-scale but not constrained by any direct physical measurements.
- We demonstrate odometry estimates competitive with classical state-of-the-art VIO methods and accurate dense reconstructions that outperform GFM-enabled SLAM methods.
Sources (1)
- [1]FoundationVIO: Enforcing Metric Scale in Feed-forward Visual-Inertial OdometryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 02:24 PM
Motivated by parallels with classical visual-inertial odometry, we introduce FoundationVIO which integrates inertial sensing with GFM predictions to recover metric scale reconstruction using a factor-graph based approach.
Feed-forward geometric foundation models (GFMs) directly predict dense geometry and camera poses from batches of images.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 10, 2026LightVAE: Towards Compact and Efficient Video Autoencoders
- Oct 10, 2026EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment
- Oct 9, 2026Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction
- Oct 9, 2026Latent World Action Model for GUI Agents
- Oct 8, 2026openai/codex rust-v0.162.0: 0.162.0
- Oct 8, 2026Understanding Jev, the new model everyone is talking about