BUGS: Universal 3D Gaussian Splatting With a Bi-Directional Gaussian Growing Mechanism

3D Gaussian Splatting has the capability for realtime and high-quality scene reconstruction, bringing the efficiency and accuracy of this task to a new level. Most previous methods improve the vanilla 3DGS by incorporating external priors such as depth priors and shape priors. However, these methods often suffer from high memory cost, distorted geometric structures, and limitations in scene density. To solve these issues, this paper proposes a new 3DGS-based method for scene reconstruction, showing stable reconstruction and better geometry in both dense and sparse scenes. We re-model the growing of the Gaussians as a bi-directional process without any external priors, adaptively increasing and decreasing the density of the Gaussians across various scenes. For this purpose, we introduce a bi-directional Gaussian growing mechanism that determines the cloning, splitting and merging operations based on the region consistency within a local receptive field. We utilize these three operations to enable adaptive bi-directional control of the density of the Gaussian points across different regions, effectively improving the geometric structure and simplifying the Gaussian points. Extensive experiments on several open benchmarks have demonstrated that our method could universally achieve comparable results with much fewer Gaussian points and better geometry structures in both dense and sparse scenes with a concise framework. We will release our codes after this paper is accepted.