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Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians

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arxiv 2405.16544 v1 pith:XUMRMMK6 submitted 2024-05-26 cs.CV

Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians

Erik Sandstr\"om , Keisuke Tateno , Michael Oechsle , Michael Niemeyer , Luc Van Gool , Martin R. Oswald , Federico Tombari This is my paper
classification cs.CV
keywords depthmethodsrgb-onlyslamdensegaussiangloballyoptimized
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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3D Gaussian Splatting has emerged as a powerful representation of geometry and appearance for RGB-only dense Simultaneous Localization and Mapping (SLAM), as it provides a compact dense map representation while enabling efficient and high-quality map rendering. However, existing methods show significantly worse reconstruction quality than competing methods using other 3D representations, e.g. neural points clouds, since they either do not employ global map and pose optimization or make use of monocular depth. In response, we propose the first RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes all benefits of globally optimized tracking by adapting dynamically to keyframe pose and depth updates by actively deforming the 3D Gaussian map. Moreover, we find that refining the depth updates in inaccurate areas with a monocular depth estimator further improves the accuracy of the 3D reconstruction. Our experiments on the Replica, TUM-RGBD, and ScanNet datasets indicate the effectiveness of globally optimized 3D Gaussians, as the approach achieves superior or on par performance with existing RGB-only SLAM methods methods in tracking, mapping and rendering accuracy while yielding small map sizes and fast runtimes. The source code is available at https://github.com/eriksandstroem/Splat-SLAM.

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Forward citations

Cited by 9 Pith papers

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

  1. WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment

    cs.RO 2026-04 unverdicted novelty 7.0

    WaterSplat-SLAM achieves robust camera tracking and high-fidelity rendering in underwater environments by coupling semantic medium filtering into two-view reconstruction and using an online medium-aware Gaussian map.

  2. ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat

    cs.RO 2025-12 conditional novelty 6.0

    ReMoSPLAT achieves reactive mobile-manipulation collision avoidance by querying distances from a Gaussian Splat reconstruction, matching a ground-truth-SDF controller in simulation.

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

    cs.CV 2026-07 conditional novelty 5.0

    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 ...

  4. GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM

    cs.RO 2026-07 conditional novelty 5.0

    GLidE-SLAM moves pose-only photometric tracking to OpenGL ES compute shaders, reporting up to 9x faster frame rates than ORB-SLAM2 on embedded platforms with comparable ATE on TUM and EuRoC sequences.

  5. MoonSplat: Monocular Online Gaussian Splatting with Sim(3) Global Optimization

    cs.CV 2026-06 unverdicted novelty 5.0

    MoonSplat adds global Sim(3) loop closure and color residual learning to voxelized online 3D Gaussian Splatting for improved monocular camera tracking and rendering quality.

  6. Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping

    cs.RO 2026-05 unverdicted novelty 5.0

    Mono-Hydra++ is a monocular RGB-IMU pipeline that constructs hierarchical 3D scene graphs in real time while reporting lower trajectory error than some RGB-D baselines on indoor datasets.

  7. WildPose: A Unified Framework for Robust Pose Estimation in the Wild

    cs.CV 2026-05 unverdicted novelty 5.0

    WildPose unifies feedforward 3D features from MASt3R with differentiable bundle adjustment for robust monocular pose estimation across dynamic, static, and low-ego-motion scenes.

  8. Online 3D Gaussian Splatting Modeling with Novel View Selection

    cs.CV 2025-08 conditional novelty 5.0

    During online Gaussian splatting SLAM, training extra on non-keyframes that view the most uncertain Gaussians improves model completeness over keyframe-only training.

  9. A Survey on 3D Gaussian Splatting

    cs.CV 2024-01 unverdicted novelty 2.0

    A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.