DistGrid: Scalable Scene Reconstruction with Distributed Multi-resolution Hash Grid

• URL: http://arxiv.org/abs/2405.04416v2
• Date: Wed, 8 May 2024 14:27:52 GMT
• Title: DistGrid: Scalable Scene Reconstruction with Distributed Multi-resolution Hash Grid
• Authors: Sidun Liu, Peng Qiao, Zongxin Ye, Wenyu Li, Yong Dou,
• Abstract summary: We propose a scalable scene reconstruction method based on joint Multi-resolution Hash Grids, named DistGrid. Our method outperforms existing methods on all evaluated large-scale scenes, and provides visually plausible scene reconstruction.
• Score: 10.458776364195796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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