DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization

TL;DR

DNGaussian is a novel framework that significantly improves the efficiency and quality of sparse-view 3D Gaussian radiance field synthesis by introducing depth normalization and regularization techniques, enabling real-time rendering with reduced costs.

Contribution

The paper proposes a depth-normalized 3D Gaussian radiance field method with regularization techniques that enhance geometry accuracy and rendering speed, outperforming existing approaches.

Findings

Achieves real-time rendering over 3000 times faster than previous methods.

Reduces training time by 25 times compared to state-of-the-art.

Maintains high-quality novel view synthesis with lower memory usage.

Abstract

Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views, yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian, a depth-regularized framework based on 3D Gaussian radiance fields, offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting, despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields, we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently, we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping, we introduce Global-Local Depth Normalization, enhancing the focus on small local depth changes. Extensive experiments on LLFF, DTU, and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods, achieving comparable or better results with significantly reduced memory cost, a $25 \times$ reduction in training time, and over $3000 \times$ faster rendering speed.