Sparse multi-view hand-object reconstruction for unseen environments
<|reference_start|>Sparse multi-view hand-object reconstruction for unseen environments: Recent works in hand-object reconstruction mainly focus on the single-view and dense multi-view settings. On the one hand, single-view methods can leverage learned shape priors to generalise to unseen objects but are prone to inaccuracies due to occlusions. On the other hand, dense multi-view methods are very accurate but cannot easily adapt to unseen objects without further data collection. In contrast, sparse multi-view methods can take advantage of the additional views to tackle occlusion, while keeping the computational cost low compared to dense multi-view methods. In this paper, we consider the problem of hand-object reconstruction with unseen objects in the sparse multi-view setting. Given multiple RGB images of the hand and object captured at the same time, our model SVHO combines the predictions from each view into a unified reconstruction without optimisation across views. We train our model on a synthetic hand-object dataset and evaluate directly on a real world recorded hand-object dataset with unseen objects. We show that while reconstruction of unseen hands and objects from RGB is challenging, additional views can help improve the reconstruction quality.<|reference_end|>
@article{pang2024sparse,
    title={Sparse multi-view hand-object reconstruction for unseen environments},
    author={Yik Lung Pang, Changjae Oh, Andrea Cavallaro},
    journal={arXiv preprint arXiv:2405.01353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01353},
    primaryClass={cs.CV}
}