🤖 AI Summary
This work addresses the unclear failure mechanisms of classical spectral descriptors—such as the Heat Kernel Signature (HKS) and Wave Kernel Signature (WKS)—in non-rigid 3D shape retrieval, particularly the lack of systematic analysis regarding the contribution of different scale components. The authors propose a frequency-scale saliency framework that, for the first time, establishes a quantitative relationship between scale intervals in spectral descriptors and retrieval performance, revealing that short-scale components predominantly drive performance while long-scale components are detrimental. By characterizing category-level scale dependencies through spectral category fingerprints, they introduce a saliency-weighted strategy to optimize retrieval. Evaluated on the SHREC'11 benchmark, the method improves mean average precision (mAP) by 0.156 for challenging categories, with gains validated as stable and reliable through cross-validation and randomized controlled experiments.
📝 Abstract
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.