Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

Primitive-based methods such as 3D Gaussian Splatting haverecently become the state-of-the-art for novel-view synthesis and relatedreconstruction tasks. Compared to neural fields, these representationsare more flexible, adaptive, and scale better to large scenes. However,the limited expressivity of individual primitives makes modeling high-frequency detail challenging. We introduce Neural Harmonic Textures, aneural representation approach that anchors latent feature vectors on avirtual scaffold surrounding each primitive. These features are interpo-lated within the primitive at ray intersection points. Inspired by Fourieranalysis, we apply periodic activations to the interpolated features, turn-ing alpha blending into a weighted sum of harmonic components. Theresulting signal is then decoded in a single deferred pass using a smallneural network, significantly reducing computational cost. Neural Har-monic Textures yield state-of-the-art results in real-time novel view syn-thesis while bridging the gap between primitive- and neural-field-basedreconstruction. Our method integrates seamlessly into existing primitive-based pipelines such as 3DGUT, Triangle Splatting, and 2DGS. We fur-ther demonstrate its generality with applications to 2D image fitting andsemantic reconstruction.