Beyond Single view Decoding: Dual-view Map Inference from Trajectories via Primal-Dual Graphs Co-generation

Digital maps are crucial for web-based location services. The continuous collection of vehicle trajectories has made trajectory data a vital source for map inference. State-of-the-art (SOTA) methods formulate trajectory-based map inference as an image processing task: they first rasterize trajectories into density-based images, then extract keypoints and infer their connections to construct the road map. Despite performing well on standard road structures, these methods still suffer from low topological accuracy in complex scenarios because i) The discrete rasterized representation struggles to capture road adjacency in multi-level road structures. ii) The limited contextual awareness of keypoint-based inferring strategy leads to connectivity misjudgment in dense road areas. To solve these limitations, we propose D2Map, a Dual-view Map Inference Framework via Primal-Dual Graphs Co-generation. To precisely encode road adjacency, we introduce the serialized trajectory view as a complement to the rasterized view to reflect traversable relationships between roads, and devise a strategy-adaptive fusion module that dynamically selects and executes the optimal fusion operator to integrate dual view representations, yielding map element embeddings. To eliminate connectivity errors, we extend road map modeling from a keypoint-centric primal graph to primal and dual graphs. In the dual graph, roads are explicitly modeled as nodes, enabling context-aware topology inference. A co-generation strategy is then employed to jointly infer both graphs while maintaining their geometric consistency. Extensive experiments on two real-world datasets demonstrate the superiority of D2Map, which outperforms SOTA baselines by 11.44% in the TOPO metric.