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 multiview 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.