MS^2Gait：面向基于 LiDAR 的步态识别的多尺度时空融合网络
MS^2Gait: A Multi-Scale Spatio-Temporal Fusion Network for LiDAR-based Gait Recognition
对做点云序列人体分析的研究者，可复用其分层空间特征提取中的四种跨尺度交互策略与基于相似度的时序增强 Transformer——后者用运动一致性动态加权帧、无需显式监督，可直接迁移到动作识别、步态/姿态时序建模等任务。
原文依据 a Similarity-based Temporal Enhancement Transformer strategy leverages multi-scale aggregation to dynamically weight frames based on motion coherence
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于 3D LiDAR 的步态识别因其对光照的鲁棒性、隐私保护能力以及远距离非接触式身份验证能力而受到越来越多的关注。然而，现有基于点云的方法存在两个关键局限：它们无法建模跨空间尺度的语义远距离关联，并且采用过于简单的时序聚合方式，难以应对步态固有的异质性。为解决上述局限，我们提出 MS^2Gait，一个专为原始点云步态识别设计的多尺度时空框架。我们的分层空间特征提取模块引入四种互补的交互策略，以显式捕获长程语义依赖并在遮挡情况下恢复结构信息。此外，基于相似度的时序增强 Transformer 策略利用多尺度聚合，根据运动一致性对帧进行动态加权，在无需显式监督的情况下有效处理时序异质性。在 SUSTech1K 和 FreeGait 上的大量评估表明，MS^2Gait 分别取得 93.5% 和 83.1% 的 Rank-1 准确率，优于此前的最优方法，同时对非步态的干扰因素表现出显著的鲁棒性。
英文原文摘要 1284 字符
3D LiDAR-based gait recognition has gained increasing attention due to its robustness to illumination, privacy preservation, and capability for long-range and non-contact identity verification. However, existing point cloud-based methods suffer from two critical limitations: they fail to model semantically distant correlations across spatial scales and employ simplistic temporal aggregation that cannot handle gait's inherent heterogeneity. To address these limitations, we propose MS^2Gait, a multi-scale spatio-temporal framework tailored for raw point cloud gait recognition. Our Hierarchical Spatial Feature Extraction module introduces four complementary interaction strategies to explicitly capture long-range semantic dependencies and recover structural information under blockage. Additionally, a Similarity-based Temporal Enhancement Transformer strategy leverages multi-scale aggregation to dynamically weight frames based on motion coherence, effectively handling temporal heterogeneity without explicit supervision. Extensive evaluations on SUSTech1K and FreeGait demonstrate that MS^2Gait achieves 93.5% and 83.1% in Rank-1 accuracy, respectively, outperforming prior state-of-the-art methods, while exhibiting significant robustness against non-gait nuisance factors.