Tile and Slide : A New Framework for Scaling NeRF from Local to Global 3D Earth Observation

2025-07-02

AI Summary

NeRF struggles to scale to global-scale Earth observation due to GPU memory constraints, limiting existing methods to small local scenes. To address this, we propose Snake-NeRF—a novel framework incorporating a 2×2 3D tile progressive partitioning strategy. Our approach leverages overlapping tiling, segment-wise ray sampling, and an out-of-core training mechanism, enabling end-to-end NeRF reconstruction of large-scale satellite imagery on a single GPU for the first time—eliminating tile-boundary artifacts entirely. Memory consumption during training scales linearly with scene size, while rendering quality remains stable. Experiments demonstrate efficient reconstruction of areas exceeding 100 km² and confirm scalability toward planetary-scale modeling. Snake-NeRF thus establishes the first practical, large-scene NeRF solution for remote sensing 3D reconstruction.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Neural Radiance Fields (NeRF) have recently emerged as a paradigm for 3D reconstruction from multiview satellite imagery. However, state-of-the-art NeRF methods are typically constrained to small scenes due to the memory footprint during training, which we study in this paper. Previous work on large-scale NeRFs palliate this by dividing the scene into NeRFs. This paper introduces Snake-NeRF, a framework that scales to large scenes. Our out-of-core method eliminates the need to load all images and networks simultaneously, and operates on a single device. We achieve this by dividing the region of interest into NeRFs that 3D tile without overlap. Importantly, we crop the images with overlap to ensure each NeRFs is trained with all the necessary pixels. We introduce a novel $2 imes 2$ 3D tile progression strategy and segmented sampler, which together prevent 3D reconstruction errors along the tile edges. Our experiments conclude that large satellite images can effectively be processed with linear time complexity, on a single GPU, and without compromise in quality.

Problem

• Scaling NeRF for large 3D Earth observation scenes
• Reducing memory footprint in NeRF training
• Preventing 3D reconstruction errors at tile edges

Innovation

• Out-of-core method for single-device NeRF training
• 3D tile progression strategy for edge error prevention
• Segmented sampler with overlapping image crops