OG-Mapping: Octree-based Structured 3D Gaussians for Online Dense Mapping
<|reference_start|>OG-Mapping: Octree-based Structured 3D Gaussians for Online Dense Mapping: 3D Gaussian splatting (3DGS) has recently demonstrated promising advancements in RGB-D online dense mapping. Nevertheless, existing methods excessively rely on per-pixel depth cues to perform map densification, which leads to significant redundancy and increased sensitivity to depth noise. Additionally, explicitly storing 3D Gaussian parameters of room-scale scene poses a significant storage challenge. In this paper, we introduce OG-Mapping, which leverages the robust scene structural representation capability of sparse octrees, combined with structured 3D Gaussian representations, to achieve efficient and robust online dense mapping. Moreover, OG-Mapping employs an anchor-based progressive map refinement strategy to recover the scene structures at multiple levels of detail. Instead of maintaining a small number of active keyframes with a fixed keyframe window as previous approaches do, a dynamic keyframe window is employed to allow OG-Mapping to better tackle false local minima and forgetting issues. Experimental results demonstrate that OG-Mapping delivers more robust and superior realism mapping results than existing Gaussian-based RGB-D online mapping methods with a compact model, and no additional post-processing is required.<|reference_end|>
@article{wang2024og-mapping:,
    title={OG-Mapping: Octree-based Structured 3D Gaussians for Online Dense
  Mapping},
    author={Meng Wang, Junyi Wang, Changqun Xia, Chen Wang, Yue Qi},
    journal={arXiv preprint arXiv:2408.17223},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17223},
    primaryClass={cs.CV}
}