Gaussian Splatting for Large-Scale Aerial Scene Reconstruction From Ultra-High-Resolution Images
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Date
2025
Authors
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Journal ISSN
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Using 3D Gaussian splatting to reconstruct large-scale aerial scenes from ultra-high-resolution images is still a challenge problem because of two memory bottlenecks - excessive Gaussian primitives and the tensor sizes for ultra-high-resolution images. In this paper, we propose a task partitioning algorithm that operates in both object and image space to generate a set of small-scale subtasks. Each subtask's memory footprints is strictly limited, enabling training on a single high-end consumer-grade GPU. More specifically, Gaussian primitives are clustered into blocks in object space, and the input images are partitioned into sub-images according to the projected footprints of these blocks. This dual-space partitioning significantly reduces training memory requirements. During subtask training, we propose a depth comparison method to generate a mask map for each sub-image. This mask map isolates pixels primarily contributed by the Gaussian primitives of the current subtask, excluding all other pixels from training. Experimental results demonstrate that our method successfully achieves large-scale aerial scene reconstruction using 9K resolution images on a single RTX 4090 GPU. The novel views synthesized by our method retain significantly more details than those from current state-of-the-art methods.
Description
CCS Concepts: Computing methodologies → Computer graphics; Machine learning
@article{10.1111:cgf.70265,
journal = {Computer Graphics Forum},
title = {{Gaussian Splatting for Large-Scale Aerial Scene Reconstruction From Ultra-High-Resolution Images}},
author = {Sun, Qiulin and Lai, Wei and Li, Yixian and Zhang, Yanci},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70265}
}