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Heat Kernel Textures represent a significant methodological advance in 3D vision by replacing traditional UV mapping with geodesic heat kernels to eliminate seams and reduce memory overhead. This approach integrates directly with Gaussian Splatting pipelines, offering a more robust representation for novel view synthesis on complex manifold surfaces. The research addresses core challenges in computer graphics and machine learning regarding texture optimization and rendering efficiency.
Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat
arXiv:2609.07557v1 Announce Type: cross Abstract: 3D Gaussian Splatting has recently revolutionised novel view synthesis as well as many other 3D vision methods and applications. Drawing inspiration from this representation, we now rethink textures to overcome the main issues of UV mapping while considerably lowering their memory footprint. Heat Kernel Textures (HKTex) eliminate UV unwrapping as well as their persistent issues of wasted UV space, seams, distortions, vertex-duplication, and varying resolution. Grounded in discrete Riemannian geometry and intrinsically defined on any manifold surface discretised as a triangular mesh, HKTex uses anisotropic heat kernels as geodesic equivalents to Gaussians. Like our kernels, also the optimisation of their position and the adaptive densification strategies were redefined to operate on the surface of the object to be textureised. Our novel representation is also fully integrated with a physically based renderer and can be optimised either from existing textures or multi-view images. Our project page and code are available at circle-group.github.io/research/HeatKernelTextures.
Abstract:3D Gaussian Splatting has recently revolutionised novel view synthesis as well as many other 3D vision methods and applications. Drawing inspiration from this representation, we now rethink textures to overcome the main issues of UV mapping while considerably lowering their memory footprint. Heat Kernel Textures (HKTex) eliminate UV unwrapping as well as their persistent issues of wasted UV space, seams, distortions, vertex-duplication, and varying resolution. Grounded in discrete Riemannian geometry and intrinsically defined on any manifold surface discretised as a triangular mesh, HKTex uses anisotropic heat kernels as geodesic equivalents to Gaussians. Like our kernels, also the optimisation of their position and the adaptive densification strategies were redefined to operate on the surface of the object to be textureised. Our novel representation is also fully integrated with a physically based renderer and can be optimised either from existing textures or multi-view images. Our project page and code are available at this http URL.
Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
arXiv:2609.07557 [cs.CV]
(or arXiv:2609.07557v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.07557 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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