Comparative Analysis of Dense RGB-D SLAM Systems Using 3D Gaussians Versus Neural Implicit Methods on Embedded Platforms

Abstract

Abstract: Simultaneous localization and mapping is essential for position tracking and scene understanding. 3D Gaussian-based map representations enable photorealistic reconstruction and real-time rendering of scenes using multiple posed cameras. We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior neural radiance field-based representations by enabling faster rendering, scale awareness, and improved trajectory tracking. Our framework enables keyframe

Research Question

How do dense RGB-D SLAM systems utilizing 3D Gaussian representations compare to neural implicit methods in terms of memory consumption and frame rate on embedded platforms using the ScanNet benchmark?

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