CandidateShift: Availability-Aware Learning for Descriptor-Free 2D-3D Matching

Descriptor-free 2D-3D matching associates image keypoints with map landmarks without using local visual descriptors. Retrieval and map sampling can remove every valid correspondence for a keypoint, changing its correct matching outcome to unmatched. We present CandidateShift, a training method that separates match availability from conditional landmark identity under candidate deletion. Geometric correspondence labels supervise whether a valid match survives. For keypoints that remain matchable, the prediction on the full candidate set is restricted and renormalized over retained landmarks to provide a soft target for the prediction on the reduced set. Both predictions come from a shared matcher and receive direct correspondence supervision. Compared with the same matcher trained with supervised candidate perturbations, CandidateShift improves conditional identity accuracy under candidate deletion and yields more accurate localization, with gains that persist under stronger outlier contamination.