Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval
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 l...
| Authors | Jianru Shen |
|---|---|
| Primary category | cs.GR |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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