SfM on-the-fly: Get better 3D from What You Capture
<|reference_start|>SfM on-the-fly: Get better 3D from What You Capture: In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance is just a recent topic of growing interest. This work builds upon the original on-the-fly SfM (Zhan et al., 2024) and presents an updated version with three new advancements to get better 3D from what you capture: (i) real-time image matching is further boosted by employing the Hierarchical Navigable Small World (HNSW) graphs, thus more true positive overlapping image candidates are faster identified; (ii) a self-adaptive weighting strategy is proposed for robust hierarchical local bundle adjustment to improve the SfM results; (iii) multiple agents are included for supporting collaborative SfM and seamlessly merge multiple 3D reconstructions into a complete 3D scene when commonly registered images appear. Various comprehensive experiments demonstrate that the proposed SfM method (named on-the-fly SfMv2) can generate more complete and robust 3D reconstructions in a high time-efficient way. Code is available at http://yifeiyu225.github.io/on-the-flySfMv2.github.io/.<|reference_end|>
@article{zhan2024sfm,
    title={SfM on-the-fly: Get better 3D from What You Capture},
    author={Zongqian Zhan, Yifei Yu, Rui Xia, Wentian Gan, Hong Xie, Giulio Perda,
  Luca Morelli, Fabio Remondino, Xin Wang},
    journal={arXiv preprint arXiv:2407.03939},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03939},
    primaryClass={cs.CV}
}