ROI-GSurFisher: Next Best View Selection for Active Gaussian Splatting Via Fisher Information of ROI-Selected Gaussian Surfels

Abstract

Next Best View (NBV) selection is critical for achieving high-quality 3D reconstruction in unknown environments. This paper presents an active NBV selection approach tailored for Gaussian Splatting (GS), a widely adopted 3D reconstruction technique that has recently gained significant attention and been extended to Simultaneous Localization and Mapping (SLAM) systems. Existing state-of-the-art NBV methods for GS focus on minimizing uncertainties of GS parameters but often fail to prioritize views that improve geometric reconstruction quality. To address this limitation, we propose an active view selection method for GS-based reconstruction, with its core being ROI-GSurFisher. This method calculates Fisher Information on Gaussian surfels selected via a Region of Interest (ROI) mechanism. Both the use of surfels for computation and the ROI constraint enhance ROI-GSurFisher's ability to evaluate geometric information gain. We further introduce a close-front view scoring module that prioritizes viewpoints conducive to high-quality reconstruction. The final NBV is selected by maximizing the combined geometric information gain and close-front score. Experimental results on 3D reconstruction of various objects and scenes demonstrate consistent qualitative and quantitative improvements. Beyond standalone 3D reconstruction, the proposed NBV method can be integrated into SLAM systems to select fewer but more valuable keyframes. Code is available at https://github.com/WW11111/ROI-GSurFisher.