LEADER cuts position error by 24.1% on Oxford RobotCar and 73.9% on NCLT by combining a projection-based geometric encoder with a truncated relative reliability loss that down-weights unreliable points.
You only train once: Learning general and distinctive 3d local descriptors.IEEE TPAMI, 45 (3):3949–3967, 2022
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization
LEADER cuts position error by 24.1% on Oxford RobotCar and 73.9% on NCLT by combining a projection-based geometric encoder with a truncated relative reliability loss that down-weights unreliable points.