Map matching is a key technology connecting positioning data with high precision road networks, but it faces challenges in noise robustness, cross regional transfer, and interpretability. To addr ess the limitations of existing methods in local global fusion, dynamic road network adaptation, and reliance on black box mod els, this paper proposes SceneGTMM, a transferable GNN Transformer dual graph interaction map matching framework based on a conformal mapping based scene relative strategy. 1) Conformal mapping based scene relative strategy: constructs trajectory centric local coordinate systems to reduce dependence on the training road network, supporting cross regional transfer and dynamic road network updates; 2) GNN Transformer dual graph interaction architecture: a GNN modeled road graph captures local topological constraints, while a Transformer modeled trajectory graph captures global temporal dependencies, and cross graph attention achieves noise suppression and semantic alignment; 3) CRF enhanced structured prediction: combines the global context of the Transformer with the topological transition constraints of CRF to improve path connectivity and robustness. Experiments show that SceneGTM achieves over 80% accuracy on multi source trajectories with positioning errors of 16 50 meters, representing a 5.3% improvement over HMM. In cross city transfer scenarios, it outperforms MTrajRec, GraphMM, and TMM, and enhances interpretability through attention and relative coordinate visualization. This study provides a new paradigm for high precision, transferable map matching for real time traffic perception and autonomous driving path planning.
Viewport-adaptive 360-degree imaging seeks to allocate a fixed sampling budget to the region a viewer is likely to observe. Existing view-biased projections increase viewport resolution through non-conformal warps, which can introduce anisotropic stretching and shear. We formulate spherical Mobius boosts as exact conformal maps for fixed-budget viewport magnification. The continuous spherical warp has quasiconformal dilatation K = 1, reallocating samples toward a target direction while preserving local angles. On a SUN360 saliency audit with 72 panoramas and 216 paired viewport targets, C1 Mobius boosting improves viewport PSNR over optimized offset cubemap on all paired cases, with case-level median gain +3.26 dB, image-level median gain +3.23 dB, and panorama-level bootstrap 95% CI [+3.15, +3.33] dB. Pareto analysis shows that this is not a free global-quality improvement: C1 trades full-sphere WS-PSNR for shear-free viewport fidelity. Prediction-error and filtering studies identify the operating envelope: strong boosts are useful for accurately targeted viewports, while large target uncertainty calls for weaker boosts or fallback. These results position Mobius boosting as a geometric primitive for prediction-conditioned foveated 360-degree resampling rather than a universal encode-once layout.