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.
随着神经辐射场（NeRF）和3D高斯点（3DGS）的发展，新颖视图合成取得了显著进展。然而，在不影响实时渲染的情况下实现高质量仍然是一个挑战，尤其是在具有视角依赖效果的基于物理的光线追踪中。最近，N维高斯（N-DG）引入了6维空间-角度表示，以更好地结合视角依赖效果，但高斯的表示和控制方案仍不理想。本文中，我们重新审视了6维高斯，并引入了6维高斯点（6DGS），通过利用6维空间中的额外方向信息优化高斯控制，增强了颜色和不透明度的表示。我们的方法与3DGS框架完全兼容，并通过更好地建模视角依赖效果和细节显著改善了实时辐射场渲染。实验表明，6DGS在性能上大幅超越了3DGS和N-DG，与3DGS相比，PSNR提升了多达15.73 dB，同时减少了66.5%的高斯点数量。