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+ + + + + + ++ Simultaneous localization and mapping (SLAM) is pivotal in robotics, with photorealistic scene reconstruction emerging as a key challenge. + To address this, we introduce Computational Alignment for Real-Time Gaussian Splatting SLAM (CaRtGS), + a novel method enhancing the efficiency and quality of photorealistic scene reconstruction in real-time environments. + Leveraging 3D Gaussian Splatting (3DGS), CaRtGS achieves superior rendering quality and processing speed, + which is crucial for scene photorealistic reconstruction. + Our approach tackles computational misalignment in Gaussian Splatting SLAM (GS-SLAM) through an adaptive strategy that optimizes training, + addresses long-tail optimization, and refines densification. + Experiments on Replica and TUM-RGBD datasets demonstrate CaRtGS's effectiveness in achieving high-fidelity rendering with fewer Gaussian primitives. + This work propels SLAM towards real-time, photorealistic dense rendering, significantly advancing photorealistic scene representation. + For the benefit of the research community, we will release the code on our project website: https://dapengfeng.github.io/cartgs. +
+ +The overview of CaRtGS.We adopt ORB-SLAM3 as a front-end tracker, severing for localization and geometry mapping. + In the photorealistic rendering back-end, we apply the proposed adaptive computational alignment strategy to enhance the 3DGS optimization process, + including fast splat backward, adaptive optimization, and opacity regularization.
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