Homographic Navigation: Geometry-Driven Camera Guidance for Deterministic Planar Capture

2026-06-22

AI Summary

This study addresses the challenge of achieving high-precision camera guidance and alignment for multiple rectangular planar regions under extremely limited annotation—requiring only a single labeled image. To this end, the authors propose a geometry-centric intra-image navigation framework that leverages homography as the central organizing variable to unify modeling, alignment, and evaluation. The method integrates intra-image augmentation to generate synthetic training data and employs a two-stage inference mechanism—comprising global detection followed by local refinement—alongside a Stable Warp training strategy. This approach substantially improves alignment accuracy even with low-resolution inputs and enables sparse keypoint localization together with sample-level confidence estimation. The work establishes a robust foundation for geometry-driven camera guidance and self-supervised learning in unconstrained video settings.

Abstract

We present homographic navigation, a geometry-centric framework for guiding camera acquisition toward precise capture of planar regions. Rather than treating homography as an output, we use it as an organizing variable that unifies learning, alignment, and evaluation. From a single annotated reference image, we generate unlimited synthetic training data via homographic augmentation and train a single-shot model for joint recognition and localization of multiple artifacts (physical objects with a rectangular planar target) through sparse keypoint prediction. To address precision under limited model input resolution, we introduce a two-pass inference scheme with global detection followed by localized refinement, and a Stable Warp training strategy that significantly improves accuracy, particularly in the high-precision regime. The model also predicts confidence estimates per predicted keypoint and per the whole sample. Experimental results demonstrate that accurate planar alignment can be achieved from minimal supervision, providing a foundation for geometry-driven camera guidance and future learning from in-the-wild video data.

Problem

• homographic navigation
• planar capture
• camera guidance
• geometric alignment
• minimal supervision

Innovation

• homographic navigation
• geometry-driven guidance
• homographic augmentation
• two-pass inference
• Stable Warp training