基于神经辐射场的主动建图系统，实现大规模室内环境高效探索。
Active Neural Mapping at Scale
- 从持续更新的神经地图中提取广义维诺图，融合几何、外观、拓扑与不确定性
- 通过维诺图顶点锚定不确定区域，实现安全路径下的自适应探索粒度
- 采用混合神经辐射场表示，在大场景下仍保持高重建精度与探索效率
我们提出一种基于NeRF的主动建图系统，能够高效且稳健地探索大规模室内环境。其核心在于从不断更新的神经地图中提取广义维诺图（GVG），实现场景几何、外观、拓扑与不确定性的协同融合。将神经地图带来的不确定区域锚定于GVG的顶点，使探索路径可自适应调整粒度，沿安全路径高效穿越未知区域。借助现代混合式NeRF表示，该系统在扩展至大规模室内环境时，仍能实现优异的重建精度、覆盖率与探索效率。多尺度实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
We introduce a NeRF-based active mapping system that enables efficient and robust exploration of large-scale indoor environments. The key to our approach is the extraction of a generalized Voronoi graph (GVG) from the continually updated neural map, leading to the synergistic integration of scene geometry, appearance, topology, and uncertainty. Anchoring uncertain areas induced by the neural map to the vertices of GVG allows the exploration to undergo adaptive granularity along a safe path that traverses unknown areas efficiently. Harnessing a modern hybrid NeRF representation, the proposed system achieves competitive results in terms of reconstruction accuracy, coverage completeness, and exploration efficiency even when scaling up to large indoor environments. Extensive results at different scales validate the efficacy of the proposed system.
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