Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.
2021), 16 pages
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
IsaacIPC couples IPC contact simulation with IsaacSim/Lab for realistic rendering and introduces GMCP for tactile contact pressure modeling, demonstrated on robotic examples.
citing papers explorer
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Monte Carlo Steklov Operators for Large-Scale Geometry Processing in the Wild
Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.
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IsaacIPC: Coupling High-Fidelity Simulation and Realistic Rendering for Contact-Rich Robotic Systems
IsaacIPC couples IPC contact simulation with IsaacSim/Lab for realistic rendering and introduces GMCP for tactile contact pressure modeling, demonstrated on robotic examples.