Sparse multi-view hand-object reconstruction for unseen environments
| Type of publication: | Conference paper |
| Citation: | Pang_CVPRW_2024 |
| Publication status: | Published |
| Booktitle: | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
| Year: | 2024 |
| Month: | June |
| URL: | https://openaccess.thecvf.com/... |
| Abstract: | 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. |
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Idiap |
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| Added by: | [UNK] |
| Total mark: | 0 |
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