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DistGrid: Scalable Scene Reconstruction with Distributed Multi-resolution Hash Grid (2405.04416v2)

Published 7 May 2024 in cs.CV

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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Authors (5)
  1. Sidun Liu (7 papers)
  2. Peng Qiao (21 papers)
  3. Zongxin Ye (3 papers)
  4. Wenyu Li (19 papers)
  5. Yong Dou (33 papers)

Summary

Explaining DistGrid: A Novel Approach for Scalable Scene Reconstruction

Overview of DistGrid

DistGrid is a method developed to address the limitations of existing scene reconstruction approaches which often struggle with large-scale environments due to GPU memory constraints and inefficiencies in model training. Existing methods, like NeRF and its variants, typically face issues in scaling up due to high memory demand or increased computational loads when handling larger or more complex scenes.

DistGrid introduces a novel approach to handling large-scale scenes by dividing the scene into multiple Axis-Aligned Bounding Boxes (AABBs) each handled by a sub-model. These sub-models are managed across multiple GPUs, effectively distributing the computational load and bypassing the memory limitations of individual GPUs.

Key Innovations in DistGrid

  • Joint Multi-resolution Hash Grids: DistGrid partitions the scene into non-overlapping AABBs and uses a novel segmented volume rendering technique. This helps in managing out-of-boundary rays efficiently without needing additional models for background processing.
  • Handling Cross-Boundary Rays: A notable challenge in using multiple sub-models is managing rays that traverse more than one partition. DistGrid handles this through segmented volume rendering, where rays that cross boundaries are split and processed in their respective partitions.
  • Distributed Processing Across GPUs: The technique utilizes multiple GPUs, allowing the model to scale up the scene size or resolution beyond the capabilities of a single GPU. This is made efficient with a minimal inter-GPU communication requirement.

Practical Implications and Advantages

The practical implications of DistGrid are significant in fields requiring detailed and large-scale digital reconstructions such as urban planning, virtual reality, and geographical information systems. Here are several advantages of using DistGrid:

  • Efficiency and Scalability: By leveraging distributed computing across multiple GPUs, DistGrid can handle larger scenes more efficiently than traditional single-GPU NeRF implementations.
  • Improved Quality: The technique offers enhanced visual quality and accuracy in reconstructed scenes as demonstrated by robust numerical results where it outperforms existing models on large-scale datasets.
  • Reduced Redundancy: Unlike previous methods that might redundantly learn overlapping areas of a scene, DistGrid's approach of strictly non-overlapping partitions and unique sub-models for each reduces redundancy.

Future Prospects

Looking ahead, DistGrid's approach opens up various avenues for improvement and application:

  • Integration with Other Data Sources: Beyond drone-captured data, incorporating various data types like street-level images, videos, or even satellite imagery could enhance the model’s utility and accuracy.
  • Real-time Processing: Future enhancements could aim for real-time processing capabilities, making DistGrid suitable for dynamic scene rendering applications like augmented reality.
  • Handling Diverse Scenes: As current tests focus on urban or suburban areas, expanding to diverse environments such as rural or natural scenes could greatly enhance the model's applicability.

Conclusion

DistGrid represents a significant step forward in scalable scene reconstruction. By optimizing GPU usage and reducing redundancy in learning multiple scene partitions, DistGrid not only enhances efficiency and scalability but also maintains high-quality scene reconstruction. The approach paves the way for advanced scene modeling applications that require the processing of extensive spatial data without compromising on detail or computation speed.

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