קולוקוויום וסמינרים

Classical shape descriptors such as Heat Kernel Signature (HKS), Wave Kernel Signature (WKS), and Signature of Histograms of Orientations (SHOT), while widely used in shape analysis, exhibit sensitivity to mesh connectivity, sampling patterns, and topological noise.

While differential geometry offers a promising alternative through its theory of differential invariants, which are theoretically guaranteed to be robust shape descriptors, the computation of these invariants on discrete meshes often leads to unstable numerical approximations, limiting their practical utility. We present a self-supervised learning approach for extracting geometric features from 3D surfaces. Our method combines synthetic data generation with a neural architecture designed to learn sampling-invariant features.

By integrating our features into existing shape correspondence frameworks, we demonstrate improved performance on standard benchmarks including FAUST, SCAPE, TOPKIDS, and SHREC'16, showing particular robustness to topological noise and partial shapes.

Deduplication is widely utilized in many modern large scale storage systems and provide an effective solution for both secondary and primary storage. Therefore, there is a rising need for deduplication storage to support advanced features such as data indexing for information retrieval. To our knowledge, no indexing solution for deduplicated storage utilizes the deduplication and current indexing methods process duplicates.

In this work, we propose IDEA, Inverted Deduplication-Aware Index, which we use to explore the potential of utilizing deduplication in keyword-indexing. IDEA is shown to be superior to the deduplication-oblivious approach, in both index creation and index size, and index query retrieval time. IDEA is also shown to be extendible for advanced indexing features, and orthogonal to the underlying index-engine.