InstantSfM: Fully Sparse and Parallel Structure-from-Motion Article Swipe
Jing Zhong
,
Zhi‐Hui Zhan
,
Quankai Gao
,
Ziyu Chen
,
Haozhe Lou
,
Jiageng Mao
,
Ulrich Neumann
,
Yue Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.13310
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.13310
Structure-from-Motion (SfM), a method that recovers camera poses and scene geometry from uncalibrated images, is a central component in robotic reconstruction and simulation. Despite the state-of-the-art performance of traditional SfM methods such as COLMAP and its follow-up work, GLOMAP, naive CPU-specialized implementations of bundle adjustment (BA) or global positioning (GP) introduce significant computational overhead when handling large-scale scenarios, leading to a trade-off between accuracy and speed in SfM. Moreover, the blessing of efficient C++-based implementations in COLMAP and GLOMAP comes with the curse of limited flexibility, as they lack support for various external optimization options. On the other hand, while deep learning based SfM pipelines like VGGSfM and VGGT enable feed-forward 3D reconstruction, they are unable to scale to thousands of input views at once as GPU memory consumption increases sharply as the number of input views grows. In this paper, we unleash the full potential of GPU parallel computation to accelerate each critical stage of the standard SfM pipeline. Building upon recent advances in sparse-aware bundle adjustment optimization, our design extends these techniques to accelerate both BA and GP within a unified global SfM framework. Through extensive experiments on datasets of varying scales (e.g. 5000 images where VGGSfM and VGGT run out of memory), our method demonstrates up to about 40 times speedup over COLMAP while achieving consistently comparable or even improved reconstruction accuracy. Our project page can be found at https://cre185.github.io/InstantSfM/.
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- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2510.13310
- https://arxiv.org/pdf/2510.13310
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415275732
All OpenAlex metadata
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https://openalex.org/W4415275732Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2510.13310Digital Object Identifier
- Title
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InstantSfM: Fully Sparse and Parallel Structure-from-MotionWork title
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preprintOpenAlex work type
- Publication year
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2025Year of publication
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2025-10-15Full publication date if available
- Authors
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Jing Zhong, Zhi‐Hui Zhan, Quankai Gao, Ziyu Chen, Haozhe Lou, Jiageng Mao, Ulrich Neumann, Yue WangList of authors in order
- Landing page
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https://arxiv.org/abs/2510.13310Publisher landing page
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https://arxiv.org/pdf/2510.13310Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2510.13310Direct OA link when available
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0Total citation count in OpenAlex
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