DistGrid: Scalable Scene Reconstruction with Distributed Multi-resolution Hash Grid
CoRR(2024)
摘要
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.
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