Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics

Camera-based perception systems for autonomous drivingare typically developed and evaluated using fixed sensor rigs, while real-world vehicle fleets exhibit substantial variation in camera placement,orientation, field of view, and camera count. This mismatch introducesa cross-rig domain gap in which only the geometric observation pro-cess changes. To study this effect under controlled conditions, we intro-duce Plentiful CARLA Camera Rigs, a benchmark that renders identicaldriving scenes under 14 systematically designed camera rigs. This setupenables direct analysis of cross-rig generalization without confoundingchanges in scene content or appearance. Using the benchmark, we ana-lyze cross-rig transfer behavior of representative multi-view perceptionarchitectures and observe substantial performance shifts induced by ge-ometric rig variation. To facilitate structured analysis, we further intro-duce two calibration-based descriptors derived from rig metadata: RigVariance, capturing internal rig diversity, and Rig Contrastive Distance,measuring geometric discrepancy between rigs. Our experiments showthat geometric rig differences strongly correlate with relative cross-rigperformance shifts and that Rig Contrastive Distance provides a reliableproxy for ranking transfer difficulty between sensor rigs.