CaRtGS: Computational Alignment for Real-Time Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2410.00486v2
• Date: Wed, 2 Oct 2024 14:07:56 GMT
• Title: CaRtGS: Computational Alignment for Real-Time Gaussian Splatting SLAM
• Authors: Dapeng Feng, Zhiqiang Chen, Yizhen Yin, Shipeng Zhong, Yuhua Qi, Hongbo Chen,
• Abstract summary: CaRtGS is a novel method enhancing the efficiency and quality of photorealistic scene reconstruction in real-time environments. Our approach tackles computational misalignment in Gaussian Splatting SLAM (GS-SLAM) through an adaptive strategy. Experiments on Replica and TUM-RGBD demonstrate CaRtGS's effectiveness in achieving high-fidelity rendering datasets.
• Score: 2.894006173981735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 release the code on our project website: https://dapengfeng.github.io/cartgs.

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