LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

Feedforward geometric foundation models achieve strongshort-window reconstruction, yet scaling them to minutes-long videos isbottlenecked by quadratic attention complexity or limited effective mem-ory in recurrent designs. We present LoGeR (Long-context GeometricReconstruction), a novel architecture that scales dense 3D reconstruc-tion to extremely long sequences without post-optimization. LoGeRprocesses video streams in chunks, leveraging strong bidirectional priorsfor high-fidelity intra-chunk reasoning. To manage the critical challengeof coherence across chunk boundaries, we propose a learning-based hybridmemory module. This dual-component system combines a parametric Test-Time Training (TTT) memory to anchor the global coordinate frame andprevent scale drift, alongside a non-parametric Sliding Window Attention(SWA) mechanism to preserve uncompressed context for high-precisionadjacent alignment. Notably, this memory architecture allows LoGeR togeneralize to sequences of thousands of frames during inference, despitebeing trained on only 128 frames. When evaluated on standard bench-marks and a repurposed VBR dataset with sequences up to 19k frames,LoGeR substantially outperforms current state-of-the-art feedforwardmethods. It reduces the ATE on KITTI by over 74% while achievingrobust, globally consistent reconstruction across unprecedented horizons.