Low-Resolution Hierarchical Training for Efficient 3D Gaussian Splatting

Abstract

3D Gaussian Splatting (3DGS) has been widely discussed due to its impressive ability to provide fast and high-fidelity reconstruction. However, the training efficiency of 3D Gaussian Splatting limits its application in real-time tasks such as Simultaneous Localization and Mapping (SLAM). In this paper, we present a low-resolution hierarchical training method for 3DGS. We show that the computational complexity of 3DGS can be dramatically reduced by down-sampling the training images. Thus, we use a low-resolution hierarchical training method to accelerate the training process, which optimizes 3D Gaussians from coarse to fine. In addition, we employ anti-alias filters to overcome the aliasing effect due to multi-resolution training. Experiments show that the proposed method outperforms existing methods in terms of training efficiency, which reduces the time consumption of the original 3DGS by 57.81% without obvious impact on the reconstruction quality.