LoopSplat: Loop Closure by Registering 3D Gaussian Splats
<|reference_start|>LoopSplat: Loop Closure by Registering 3D Gaussian Splats: Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian Splats (3DGS) has recently shown promise towards more accurate, dense 3D scene maps. However, existing 3DGS-based methods fail to address the global consistency of the scene via loop closure and/or global bundle adjustment. To this end, we propose LoopSplat, which takes RGB-D images as input and performs dense mapping with 3DGS submaps and frame-to-model tracking. LoopSplat triggers loop closure online and computes relative loop edge constraints between submaps directly via 3DGS registration, leading to improvements in efficiency and accuracy over traditional global-to-local point cloud registration. It uses a robust pose graph optimization formulation and rigidly aligns the submaps to achieve global consistency. Evaluation on the synthetic Replica and real-world TUM-RGBD, ScanNet, and ScanNet++ datasets demonstrates competitive or superior tracking, mapping, and rendering compared to existing methods for dense RGB-D SLAM. Code is available at loopsplat.github.io.<|reference_end|>
@article{zhu2024loopsplat:,
    title={LoopSplat: Loop Closure by Registering 3D Gaussian Splats},
    author={Liyuan Zhu and Yue Li and Erik Sandstr"om and Shengyu Huang and
  Konrad Schindler and Iro Armeni},
    journal={arXiv preprint arXiv:2408.10154},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10154},
    primaryClass={cs.CV cs.RO}
}