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CaRtGS: Computational Alignment for Real-Time Gaussian Splatting SLAM

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arxiv 2410.00486 v4 pith:ANYUEGLL submitted 2024-10-01 cs.CV cs.RO

Dapeng Feng , Zhiqiang Chen , Yizhen Yin , Shipeng Zhong , Yuhua Qi , Hongbo Chen This is my paper · ORCID
classification cs.CVcs.RO
keywords cartgsgaussianphotorealisticslamreal-timescenesplattingcomputational
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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 enhances optimization iterations, addresses long-tail optimization, and refines densification. Experiments on Replica, TUM-RGBD, and VECtor 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 and accompanying videos on our project website: https://dapengfeng.github.io/cartgs.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On-the-fly Reconstruction for Large-Scale Novel View Synthesis from Unposed Images

    cs.CV 2025-06 conditional novelty 6.0 of 10

    An incremental pipeline jointly estimates camera poses and 3D Gaussian primitives at capture speed, handling large ordered photo sequences with anchors and progressive merging.

  2. NSL-SLAM: High-Fidelity Neural Structured-Light Depth for Practical SLAM and Reconstruction

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Injecting frozen monocular depth features into neural structured-light decoding cuts Replica-SL depth RMSE ~35% vs NSL and, with depth-centric GICP+sparse anchors+light BA, yields the most stable real D435 SLAM among ...

  3. Monocular Online Reconstruction with Enhanced Detail Preservation

    cs.GR 2025-05 conditional novelty 5.0 of 10

    A monocular online mapper seeds 3D Gaussians from tracker features and error regions, prunes them with multi-level occupancy hashing, and balances local and global optimization to exceed prior RGB-only and most RGB-D ...

  4. RP-SLAM: Real-time Photorealistic SLAM with Efficient 3D Gaussian Splatting

    cs.RO 2024-12 conditional novelty 5.0 of 10

    RP-SLAM reports state-of-the-art rendering quality and compact model size for 3DGS-based SLAM by combining gradient-guided adaptive sampling, KNN filtering, a dynamic keyframe window, and sparse-point-cloud monocular ...

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