Classical spectral descriptors such as the Heat Kernel Signature and Wave Kernel Signature are widely used for non-rigid 3D shape retrieval, yet their failure modes remain poorly understood. We present a frequency-scale saliency framework that audits these descriptors by quantifying the retrieval-level contribution of each descriptor scale interval through ablation. We introduce class spectral fingerprints to characterize category-level scale dependence, and show that descriptor similarity between class pairs is substantially correlated with retrieval failure, with a Spearman correlation of 0.479. Experiments on SHREC'11 demonstrate that short scales dominate retrieval performance while long scales are harmful, that HKS and WKS exhibit distinct scale dependence patterns, and that saliency-weighted retrieval improves mAP on hard categories by 0.156, with cross-fold and random-weight controls confirming that the gain is stable and not due to arbitrary reweighting.

翻译：经典谱描述符（如热核签名与波核签名）广泛应用于非刚性三维形状检索，但其失效机制尚不明确。本文提出频率尺度显著性框架，通过消融实验量化各描述符尺度区间对检索结果的贡献，实现对描述符的审计。我们引入类别谱指纹以表征类别级尺度依赖特性，并揭示描述符相似度与检索失败之间存在显著相关性（斯皮尔曼相关系数达0.479）。在SHREC'11数据集上的实验表明：短尺度主导检索性能而长尺度产生负面影响，热核签名与波核签名呈现迥异的尺度依赖模式。基于显著性的加权检索将困难类别的平均精度提升0.156，经交叉验证与随机权重控制实验证实，该提升具有稳定性且非源于权重任意调整。