Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstruction tasks. Compared to neural fields, these representations are 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, a neural representation approach that anchors latent feature vectors on a virtual scaffold surrounding each primitive. These features are interpolated within the primitive at ray intersection points. Inspired by Fourier analysis, we apply periodic activations to the interpolated features, turning alpha blending into a weighted sum of harmonic components. The resulting signal is then decoded in a single deferred pass using a small neural network, significantly reducing computational cost. Neural Harmonic Textures yield state-of-the-art results in real-time novel view synthesis while bridging the gap between primitive- and neural-field-based reconstruction. Our method integrates seamlessly into existing primitive-based pipelines such as 3DGUT, Triangle Splatting, and 2DGS. We further demonstrate its generality with applications to 2D image fitting and semantic reconstruction.

翻译：基于基元的方法（如3D高斯溅射）近期已成为新视角合成及相关重建任务中的最先进技术。与神经场相比，这些表示更灵活、自适应，并能更好地扩展至大规模场景。然而，单个基元的有限表达能力使得建模高频细节具有挑战性。我们引入神经谐波纹理，这是一种神经表示方法，将潜在特征向量锚定在每个基元周围的虚拟支架上。这些特征在基元内的光线交点处进行插值。受傅里叶分析启发，我们对插值后的特征应用周期性激活，将阿尔法混合转化为谐波分量的加权和。随后，使用一个小型神经网络在单次延迟渲染过程中解码生成的信号，显著降低计算成本。神经谐波纹理在实时新视角合成中取得了最先进的结果，同时弥合了基于基元与基于神经场的重建之间的差距。我们的方法无缝集成到现有的基于基元的流程中，如3DGUT、三角溅射和2DGS。我们进一步通过其在二维图像拟合和语义重建中的应用证明了其通用性。