FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction

Vision-based 3D occupancy prediction fundamentally relieson the 2D-to-3D view transformation. Current paradigms predominantlyutilize explicit physical projection, which artificially restricts the routingmatrix to strict, sparse camera rays. While computationally efficient, thisimposes a severe Locality Bottleneck, preventing the network from con-structing holistic contextual understanding and degrading sharply whencamera extrinsics are unreliable or absent. To break this bottleneck, weabstract view transformation as unconstrained bipartite routing and pro-pose Factorized Dense Routing (FDR). By approximating dense2D-to-3D mixing through hierarchical tensor contractions, FDR guar-antees a fully-global receptive field with tractable, sub-quadratic com-plexity. Crucially, the mandatory spatial contraction in dense routingexposes a fundamental Resolution-Context Trade-off. To address this,we introduce a Resolution-Context Decoupled Architecture. Wefactorize the 3D space into a global macroscopic topological anchor (viaFDR) and precise local geometric planes (via explicit projection). Thisdecoupling enables global semantic inference and exact surface localiza-tion to complement each other without mutual compromise. Extensiveexperiments demonstrate that our framework achieves state-of-the-artperformance on the Occ3D-nuScenes and Occ3D-Waymo benchmarks.More notably, in an uncalibrated setting where physical extrinsics arewithheld, our global routing internalizes the implicit multi-camera rigtopology and exhibits substantially stronger structural robustness thanphysical-projection baselines under the same protocol.