Sparse multi-view hand-object reconstruction for unseen environments
arxiv(2024)
摘要
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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