Neural Radiance Field~(NeRF) achieves extremely high quality in object-scaled and indoor scene reconstruction. However, there exist some challenges when reconstructing large-scale scenes. MLP-based NeRFs suffer from limited network capacity, while volume-based NeRFs are heavily memory-consuming when the scene resolution increases. Recent approaches propose to geographically partition the scene and learn each sub-region using an individual NeRF. Such partitioning strategies help volume-based NeRF exceed the single GPU memory limit and scale to larger scenes. However, this approach requires multiple background NeRF to handle out-of-partition rays, which leads to redundancy of learning. Inspired by the fact that the background of current partition is the foreground of adjacent partition, we propose a scalable scene reconstruction method based on joint Multi-resolution Hash Grids, named DistGrid. In this method, the scene is divided into multiple closely-paved yet non-overlapped Axis-Aligned Bounding Boxes, and a novel segmented volume rendering method is proposed to handle cross-boundary rays, thereby eliminating the need for background NeRFs. The experiments demonstrate that our method outperforms existing methods on all evaluated large-scale scenes, and provides visually plausible scene reconstruction. The scalability of our method on reconstruction quality is further evaluated qualitatively and quantitatively.

翻译：神经辐射场（NeRF）在物体级和室内场景重建中实现了极高的质量。然而，在重建大规模场景时仍面临一些挑战。基于多层感知机（MLP）的NeRF受限于网络容量，而基于体素的NeRF在场景分辨率提升时会消耗大量内存。近期方法提出对场景进行地理分区，并为每个子区域训练独立的NeRF。这种分区策略有助于基于体素的NeRF突破单GPU内存限制，从而扩展到更大规模场景。然而，该方法需要多个背景NeRF处理跨区域射线，导致学习冗余。受"当前分区背景即为相邻分区前景"这一事实启发，我们提出了一种基于联合多分辨率哈希网格的可扩展场景重建方法——DistGrid。该方法将场景划分为多个紧密排列且不重叠的轴对齐包围盒，并设计了一种新颖的分段体渲染方法处理跨边界射线，从而消除对背景NeRF的需求。实验表明，在评估的所有大规模场景中，我们的方法均优于现有方法，并能提供视觉上合理的场景重建结果。此外，我们还通过定性和定量评估进一步验证了该方法在重建质量方面的可扩展性。