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

Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Terrence Chen, Ziyan Wu

Keywords: 3D Gaussian splatting, 6D Gaussian splatting, volumetric rendering

TL;DR: The paper introduces 6D Gaussian Splatting (6DGS) for real-time radiance field rendering. It achieves up to a 15.73 dB PSNR boost while using 66.5% fewer Gaussian points over 3DGS.

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 rendering using ray/path 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/.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 1975