Gaussian-Plus-SDF SLAM: High-fidelity 3D Reconstruction at 150+ fps

• URL: http://arxiv.org/abs/2509.11574v1
• Date: Mon, 15 Sep 2025 04:37:32 GMT
• Title: Gaussian-Plus-SDF SLAM: High-fidelity 3D Reconstruction at 150+ fps
• Authors: Zhexi Peng, Kun Zhou, Tianjia Shao,
• Abstract summary: GPS-SLAM (Gaussian-Plus-SDF SLAM) is a real-time 3D reconstruction system achieving over 150 fps on real-world Azure Kinect sequences.
• Score: 28.26214568008528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent Gaussian-based SLAM methods achieve photorealistic reconstruction from RGB-D data, their computational performance remains a critical bottleneck. State-of-the-art techniques operate at less than 20 fps, significantly lagging behind geometry-centric approaches like KinectFusion (hundreds of fps). This limitation stems from the heavy computational burden: modeling scenes requires numerous Gaussians and complex iterative optimization to fit RGB-D data, where insufficient Gaussian counts or optimization iterations cause severe quality degradation. To address this, we propose a Gaussian-SDF hybrid representation, combining a colorized Signed Distance Field (SDF) for smooth geometry and appearance with 3D Gaussians to capture underrepresented details. The SDF is efficiently constructed via RGB-D fusion (as in geometry-centric methods), while Gaussians undergo iterative optimization. Our representation enables drastic Gaussian reduction (50% fewer) by avoiding full-scene Gaussian modeling, and efficient Gaussian optimization (75% fewer iterations) through targeted appearance refinement. Building upon this representation, we develop GPS-SLAM (Gaussian-Plus-SDF SLAM), a real-time 3D reconstruction system achieving over 150 fps on real-world Azure Kinect sequences -- delivering an order-of-magnitude speedup over state-of-the-art techniques while maintaining comparable reconstruction quality. We will release the source code and data to facilitate future research.

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