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LoGeR scales feedforward dense 3D reconstruction to extremely long videos. By processing video streams in chunks and bridging them with a novel hybrid memory module, LoGeR alleviates quadratic complexity bottlenecks. It combines Sliding Window Attention (SWA) for precise local alignment with Test-Time Training (TTT) for long-range global consistency, reducing drift over massive sequences up to 19,000 frames without any post-hoc optimization.
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