AIIC AI Intelligence Centre

SOURCE-LINKED INTELLIGENCE

Accelerating HKTex without Mesh Eigensystems: Local Unfolding and Randomized Thermal Features

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Heat Kernel Textures (HKTex) represent surface appearance with intrinsic anisotropic kernels, but evaluate them using 50 global Laplace-Beltrami eigendecompositions and a resident basis of shape [50,V,256]. We study two complementary ways to remove this bottleneck while leaving the trainer, GeodesicOpt, density control, and compositing unchanged. LocalHK exploits the measured locality of trained kernels and replaces spectral evaluation by radius-bounded hinge unfolding and an analytic log-map kernel. On 10 Objaverse meshes and an 8-mesh low-poly holdout, it changes mean view PSNR from 31.35 to

Read original source ↗ Open in workspace

recordType
paper
region
Global

Evidence & attribution

First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.