神经戈尔巴式点云：通过神经戈尔巴增强高频率表面重建
Neural Gabor Splatting: Enhanced Gaussian Splatting with Neural Gabor for High-frequency Surface Reconstruction
AI总结：
本文提出神经戈尔巴点云方法，通过在高斯点云中加入轻量级多层感知机来增强颜色变化的表示，减少点云数量，实现高频率表面的精确重建。
摘要：
近年来，3D高斯点云（3DGS）作为3D重建和视点合成的强大方法迅速发展。其显式表示允许快速训练、实时渲染和方便的后期处理，如编辑和表面重建。然而，3DGS存在关键缺点：高频率外观细节场景中点云数量急剧增长，因为每个点云只能表示单色，需要多个点云来表示每个尖锐颜色转换。为克服这一限制，我们提出了神经戈尔巴点云，通过在每个高斯点云中加入轻量级多层感知机，模型在单个点云内广泛的颜色变化。为进一步控制点云数量，我们引入了频率感知的密集策略，根据频率能量选择不匹配的点云进行修剪和克隆。我们的方法实现了对挑战性高频率表面的准确重建。我们通过在标准基准（如Mip-NeRF360和高频率数据集（如棋格图案））上的广泛实验，以及全面的消融研究，展示了其有效性。
英文摘要：
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.
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