提出新型帧级门控机制，显著提升长序列3D重建的精度与稳定性。
Rethinking the State Update Gate for Long-Sequence Recurrent 3D Reconstruction
- 设计可微分的帧级门控α_t，基于帧间特征变化动态调节每帧贡献度。
- 在长达4541帧的序列上，定位误差降低51%，深度估计误差减少12.8%。
- 无需训练、零额外计算，适合资源受限的实时3D重建场景。
在严格恒定内存预算下，流式3D重建的性能取决于递归状态随时间的更新方式。我们对五种基准上的TTT3R型逐标记门控进行分析，发现其存在结构性瓶颈：门控值幅度受限（中位数0.31；最大不超过0.6），且几乎不随帧变化，导致每个状态标记的有效记忆时长仅约3帧，成为长序列漂移的根本原因。问题根源在于：现有推理时方法仅在帧内逐标记层面调节更新，而忽略了帧间维度的关键问题——每帧应以多强权重影响状态。为此，我们提出一种标量帧级门控α_t ∈ (0,1]，通过帧间内部特征变化的闭式解析推导获得，是对经典SLAM关键帧选择的连续松弛，无需参数、无需训练、无需额外前向传播。在涵盖相机位姿、视频深度和3D重建的六个基准上，该方法在最长4541帧的TUM-RGBD位姿序列上将ATE降低51%，在Bonn视频深度任务中使AbsRel下降12.8%，在KITTI长序列位姿估计中超越LongStream与Keyframe-VO，同时保持恒定内存且无训练成本。
原文摘要 · Abstract (English)
Streaming 3D reconstruction under a strict constant-memory budget hinges on how the recurrent state is updated as the stream evolves. We profile TTT3R-style per-token gates across five benchmarks and discover a structural bottleneck: the gate is intrinsically bounded in magnitude (median $0.31$; never exceeding $0.6$) and nearly frame-invariant, yielding an effective memory horizon of only $\sim$3 frames per state token, which serves as the structural origin of long-sequence drift. We trace this to a missing axis: existing inference-time methods modulate updates only at the per-token, intra-frame level, while the orthogonal frame-level question of \emph{how strongly each frame should contribute to the state} has been treated as content-independent. We close this gap with a scalar frame-level gate $α_t \in (0, 1]$ derived in closed form from frame-to-frame changes of internal features -- a continuous relaxation of classical Simultaneous Localization and Mapping (SLAM) keyframe selection that requires no parameters, no training, and no extra forward pass. Across six benchmarks spanning camera pose, video depth, and 3D reconstruction at sequence lengths up to $4,541$ frames, our gate cuts ATE by $51\%$ on long TUM-RGBD pose sequences, reduces AbsRel by $12.8\%$ on Bonn video depth, and on KITTI long-sequence pose estimation surpasses both LongStream and Keyframe-VO, while retaining strictly constant memory at zero training cost.
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