How does the computational throughput of semantic-guided diffusion fusion in multimodal VSLAM systems scale wi

Abstract

Abstract: Recently neural radiance fields (NeRF) have been widely exploited as 3D representations for dense simultaneous localization and mapping (SLAM). Despite their notable successes in surface modeling and novel view synthesis, existing NeRF-based methods are hindered by their computationally intensive and time-consuming volume rendering pipeline. This paper presents an efficient dense RGB-D SLAM system, i.e., CG-SLAM, based on a novel uncertainty-aware 3D Gaussian field with high consistency and geometric stability. Through an in-depth analysis of Gaussian Splatting, we propose several techniques t

Research Question

How does the computational throughput of semantic-guided diffusion fusion in multimodal VSLAM systems scale with increasing input resolution compared to early fusion baselines, measured using the FPS metric on standard benchmarks like KITTI or TUM RGB-D?

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