What Classifier Recovery Reveals about Cross-View Generalization

A visual recognition system can fail after a change in camera viewpoint yet recover substantial accuracy by refitting its classifier on labelled target data while keeping the feature extractor frozen. Such recovery establishes accessible class information, but does not establish preserved representation geometry or a portable decision rule. We investigate this distinction through a diagnostic decomposition of local linear recoverability, class-relational organization, and cross-view decision compatibility across five task-trained convolutional architectures and frozen DINOv2 representations. Under cross-dataset transfer, convolutional classifiers retain 48.1–57.8% accuracy for a broadly comparable side-right view but fall to 6.9–12.1% for a frontal view; classifier-only frontal refitting recovers 47.1–59.4%, while DINOv2 rises from 11.8% to 94.1% under its reference protocol. Direct frontal–side-right geometry within 100-Driver shows a distinct structural regime: DINOv2 crop features retain substantially greater class-relational correspondence than the source-to-frontal cross-dataset comparison, with sensitivity to representation, input treatment, and covariance reference. Shared classifiers nevertheless remain view-asymmetric, and recovery decreases under driver-disjoint evaluation. The resulting diagnosis separates recoverable class information from class-relational change and from the functional compatibility of a fitted decision rule. These properties require separate measurements under explicit population contracts. This provides a diagnostic basis for evaluating single-source, closed-set cross-view methods beyond their target recovery score.