Recent years have witnessed remarkable success of 3D Gaussian Splatting (3DGS) in novel view synthesis, surpassing prior differentiable rendering methods in both quality and efficiency. However, its training process suffers from coupled opacity-color optimization that frequently converges to local minima, producing floater artifacts that degrade visual fidelity. We present StableGS, a framework that eliminates floaters through cross-view depth consistency constraints while introducing a dual-opacity GS model to decouple geometry and material properties of translucent objects. To further enhance reconstruction quality in weakly-textured regions, we integrate DUSt3R depth estimation, significantly improving geometric stability. Our method fundamentally addresses 3DGS training instabilities, outperforming existing state-of-the-art methods across open-source datasets.
View on arXiv@article{wang2025_2503.18458,
title={ StableGS: A Floater-Free Framework for 3D Gaussian Splatting },
author={ Luchao Wang and Qian Ren and Kaimin Liao and Hua Wang and Zhi Chen and Yaohua Tang },
journal={arXiv preprint arXiv:2503.18458},
year={ 2025 }
}