6DGS: Enhanced Direction-Aware Gaussian Splatting for Volumetric Rendering

TL;DR

This paper introduces 6DGS, an improved Gaussian splatting method that enhances view-dependent effects and detail rendering in real-time volumetric synthesis, outperforming previous approaches in quality and efficiency.

Contribution

We propose 6D Gaussian Splatting (6DGS), which refines color and opacity modeling and leverages directional information for better control, compatible with existing 3DGS frameworks.

Findings

Achieves up to 15.73 dB PSNR improvement over 3DGS.

Reduces Gaussian points by 66.5% compared to 3DGS.

Significantly improves real-time view synthesis quality.

Abstract

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. The project page is: https://gaozhongpai.github.io/6dgs/