H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-time Dense Mapping Using Hierarchical Hybrid Representation
<|reference_start|>H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-time Dense Mapping Using Hierarchical Hybrid Representation: In recent years, implicit online dense mapping methods have achieved high-quality reconstruction results, showcasing great potential in robotics, AR/VR, and digital twins applications. However, existing methods struggle with slow texture modeling which limits their real-time performance. To address these limitations, we propose a NeRF-based dense mapping method that enables faster and higher-quality reconstruction. To improve texture modeling, we introduce quasi-heterogeneous feature grids, which inherit the fast querying ability of uniform feature grids while adapting to varying levels of texture complexity. Besides, we present a gradient-aided coverage-maximizing strategy for keyframe selection that enables the selected keyframes to exhibit a closer focus on rich-textured regions and a broader scope for weak-textured areas. Experimental results demonstrate that our method surpasses existing NeRF-based approaches in texture fidelity, geometry accuracy, and time consumption. The code for our method will be available at: https://github.com/SYSU-STAR/H3-Mapping.<|reference_end|>
@article{jiang2024h3-mapping:,
    title={H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-time Dense
  Mapping Using Hierarchical Hybrid Representation},
    author={Chenxing Jiang, Yiming Luo, Boyu Zhou, Shaojie Shen},
    journal={arXiv preprint arXiv:2403.10821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10821},
    primaryClass={cs.RO}
}