SigmoidGS: To Guide Depth More Effectively

Abstract

The recent success of 3D Gaussian Splatting (3DGS) on the task of novel view synthesis has amazed every one with its photorealistic results with high training and rendering speed. This paper aims to increase the interpretability of the model in both geometric attribute and appearances by a simple yet effective method: lifting constraints on color features of each splat. This enables more accurate guidance from monocular depth prediction models and strengthen the ability of model to reconstruct scene geometry. Experiments shows that this effectively reduces indeterminacy of the reconstruction problem and allows better understanding of the scene structure and individual, especially in scenes with limited viewpoints, while maintaining high-fidelity rendering even in some of the uncovered views.