OccWeave: Structure-Guided Refinement and Fusion for Cooperative Semantic Occupancy

Cooperative semantic occupancy prediction combines observations from connected vehicles to recover dense scene geometry and semantics. Yet collaboration can leave distorted boundaries, missing thin structures, and fragmented local regions in the final occupancy prediction. Preserving these structures requires both informative local features and effective integration of observations from different agents. We propose OccWeave, a framework that introduces visual structural priors to connect local feature refinement with cooperative evidence weighting. Structure-Prior Feature Refinement (SPFR) associates prior features with each agent's local 3D representation and incorporates them through a gated residual. Structure-aware Cooperative Reliability Fusion (SCRF) uses the refined features, prior-induced residuals, cross-agent discrepancies, and relative pose to estimate local source weights. We realize OccWeave through a tri-plane-to-voxel architecture following CoHFF and a Gaussian-based architecture following VOGS-CP. On Semantic-OPV2V, the voxel and Gaussian implementations improve overall mIoU by 4.35 and 2.39 percentage points over their respective baselines. Both implementations also improve semantic prediction on ego-invisible non-empty voxels, in class-boundary bands, and across the thin-structure class group relative to their respective baselines.