Novel view synthesis has advanced significantly with the development of neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS). However, achieving high quality without compromising real-time rendering remains challenging, particularly for physically-based ray tracing with view-dependent effects. Recently, N-dimensional Gaussians (N-DG) introduced a 6D spatial-angular representation to better incorporate view-dependent effects, but the Gaussian representation and control scheme are sub-optimal. In this paper, we revisit 6D Gaussians and introduce 6D Gaussian Splatting (6DGS), which enhances color and opacity representations and leverages the additional directional information in the 6D space for optimized Gaussian control. Our approach is fully compatible with the 3DGS framework and significantly improves real-time radiance field rendering by better modeling view-dependent effects and fine details. Experiments demonstrate that 6DGS significantly outperforms 3DGS and N-DG, achieving up to a 15.73 dB improvement in PSNR with a reduction of 66.5% Gaussian points compared to 3DGS.

翻译：随着神经辐射场（NeRF）和3D高斯泼溅（3DGS）的发展，新视角合成技术已取得显著进步。然而，在不牺牲实时渲染性能的前提下实现高质量渲染仍然具有挑战性，尤其是在处理具有视角依赖效应的基于物理的光线追踪时。近期，N维高斯（N-DG）引入了6D空间-角度表示以更好地融入视角依赖效应，但其高斯表示与控制方案并非最优。本文重新审视了6D高斯表示，并提出了6D高斯泼溅（6DGS），该方法增强了颜色与不透明度的表示，并利用6D空间中的额外方向信息来优化高斯控制。我们的方法完全兼容3DGS框架，并通过更好地建模视角依赖效应与细节特征，显著提升了实时辐射场渲染的质量。实验表明，6DGS在性能上显著优于3DGS与N-DG，相较于3DGS，其峰值信噪比（PSNR）最高提升15.73 dB，同时高斯点数量减少了66.5%。