Recent years have witnessed the rapid emergence of 3D Gaussian splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. Its explicit representation with Gaussian primitives enables fast training, real-time rendering, and convenient post-processing such as editing and surface reconstruction. However, 3DGS suffers from a critical drawback: the number of primitives grows drastically for scenes with high-frequency appearance details, since each primitive can represent only a single color, requiring multiple primitives for every sharp color transition. To overcome this limitation, we propose neural Gabor splatting, which augments each Gaussian primitive with a lightweight multi-layer perceptron that models a wide range of color variations within a single primitive. To further control primitive numbers, we introduce a frequency-aware densification strategy that selects mismatch primitives for pruning and cloning based on frequency energy. Our method achieves accurate reconstruction of challenging high-frequency surfaces. We demonstrate its effectiveness through extensive experiments on both standard benchmarks, such as Mip-NeRF360 and High-Frequency datasets (e.g., checkered patterns), supported by comprehensive ablation studies.

翻译：近年来，三维高斯溅射（3DGS）作为三维重建和新视角合成的一种强大方法迅速涌现。其利用高斯原语的显式表示实现了快速训练、实时渲染以及便捷的后处理（如编辑和表面重建）。然而，3DGS存在一个关键缺陷：对于具有高频外观细节的场景，原语数量急剧增加，因为每个原语只能表示单一颜色，导致每个锐利色彩过渡都需要多个原语。为克服这一局限，我们提出神经加博尔溅射，该方法为每个高斯原语附加一个轻量级多层感知器，使其能在一个原语内建模广泛的色彩变化。为进一步控制原语数量，我们引入一种频率感知的致密化策略，基于频率能量选择不匹配的原语进行剪枝和克隆。我们的方法能够精确重建具有挑战性的高频表面。通过在标准基准数据集（如Mip-NeRF360）和高频数据集（如棋盘格图案）上的大量实验，结合全面的消融研究，验证了其有效性。