H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-time Dense Mapping Using Hierarchical Hybrid Representation
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要