HDA-GS: Hierarchical Density-Controlled for Anisotropic 3D Gaussian Splatting

Abstract

Recently, 3D Gaussian Splatting (3D-GS) has demonstrated impressive results in novel view synthesis, achieving outstanding rendering quality and speed. However, 3D-GS heavily relies on the quality of the initial point cloud, and its original Adaptive Density Control (ADC) module has difficulty handling regions with sparse initial points. This issue arises because larger Gaussians typically result in lower opacity under the current ADC process. Consequently, multiple Gaussian primitives tend to overlap, leading to redundant coverage of the same spatial region. To address this problem, we propose HDA-GS, which effectively increases point density in the regions of sparse initial point cloud. Simultaneously, we introduce anisotropic opacity to enhance the ability to represent details in images under complex environments. Our method provides a plug-and-play solution that can be seamlessly incorporated into existing 3D-GS techniques. Extensive qualitative and quantitative experiments on challenging datasets, including Mip-NeRF360, Tanks&Temples, and DeepBlending, confirm that our method achieves state-of-the-art rendering quality while maintaining real-time performance.