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Fully Geometric Panoramic Localization

Junho Kim · Jiwon Jeong · Young Min Kim

Arch 4A-E Poster #123
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Fri 21 Jun 10:30 a.m. PDT — noon PDT

Abstract: We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines.Given a pre-captured 3D map, our approach localizes a panorama image, taking advantage of the holistic $360^\circ$ views.The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors.However, as lines alone can be ambiguous, we express distinctive yet compact spatial contexts from relationships between lines, namely the dominant directions of parallel lines and the intersection between non-parallel lines.The resulting representations are efficient in processing time and memory compared to conventional visual descriptor-based methods.Given the groups of dominant line directions and their intersections, we can further accelerate the search process to test thousands of pose candidates in less than a millisecond without sacrificing accuracy.We empirically show that the proposed 2D-3D matching can localize panoramas for challenging scenes with similar structures, dramatic domain shifts or illumination changes.Our fully geometric approach does not involve extensive parameter tuning or neural network training, making it a practical algorithm that can be readily deployed in the real world.

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