Fully Geometric Panoramic Localization
Junho Kim Jiwon Jeong Young Min Kim
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 view. 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 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. Project page including the code
is available through this link: https://82magnolia.github.io/fgpl/.