Unified Neural Gaussian SLAM with Feature Splatting

Abstract

Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated impressive progress in high-fidelity scene reconstruction within visual SLAM. However, existing approaches often suffer from scene inconsistency, leading to visual artifacts, and the explicit maintenance of millions of Gaussians imposes significant storage overhead. To address these limitations, we present a unified Neural Gaussian SLAM with feature splatting, which represents the spatial scene as a coherent feature space while encoding view direction, distance, and position into neural Gaussians. Arbitrary image modalities-including color, depth, normals, semantics, and even language-can be decoded from this feature space. Extensive evaluations on several challenging datasets show that our method achieves state-of-the-art performance in rendering quality, reconstruction accuracy, and pose estimation.