用轻量网络提前筛选关键帧，大幅降低单目SLAM计算开销。
Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring
- 先用轻量网络预测几何价值，决定是否继续处理
- 在标准数据集上减少超85%的计算量，速度提升5倍
- 可直接接入现有系统，适合实时部署场景
几何基础模型（GFMs）近期提升了单目SLAM性能，提供了鲁棒且无需标定的3D先验。然而，在稠密视频流上部署这些模型会带来显著的计算冗余。当前基于GFM的SLAM系统通常依赖事后关键帧选择，必须进行昂贵的稠密几何解码，才能判断帧是否包含新几何信息，导致延迟拒绝和计算浪费。为此，我们提出LeanGate——一个轻量级前馈帧门控网络。LeanGate在重负荷的GFM特征提取与匹配阶段前，预测几何效用分数，评估帧的建图价值。作为可插拔模块，该方法可跳过超过90%的冗余帧。在标准SLAM基准上的评估显示，LeanGate将跟踪计算量（FLOPs）降低超过85%，实现5倍端到端吞吐量提升，同时保持稠密基线的追踪与建图精度。
原文摘要 · Abstract (English)
Geometric Foundation Models (GFMs) have recently advanced monocular SLAM by providing robust, calibration-free 3D priors. However, deploying these models on dense video streams introduces significant computational redundancy. Current GFM-based SLAM systems typically rely on post hoc keyframe selection. Because of this, they must perform expensive dense geometric decoding simply to determine whether a frame contains novel geometry, resulting in late rejection and wasted computation. To mitigate this inefficiency, we propose LeanGate, a lightweight feed-forward frame-gating network. LeanGate predicts a geometric utility score to assess a frame's mapping value prior to the heavy GFM feature extraction and matching stages. As a predictive plug-and-play module, our approach bypasses over 90% of redundant frames. Evaluations on standard SLAM benchmarks demonstrate that LeanGate reduces tracking FLOPs by more than 85% and achieves a 5x end-to-end throughput speedup. Furthermore, it maintains the tracking and mapping accuracy of dense baselines. Project page: https://lean-gate.github.io/
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