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.

在稀疏多视图设置中，我们考虑了手-物体重建问题，给定同时捕获的手和物体的多个RGB图像，我们的模型SVHO将每个视图的预测融合成统一的重建，通过在合成手-物体数据集上训练，并在真实世界记录的未见过的物体的手-物体数据集上进行直接评估，我们显示出附加视图可以提高重建质量。

用于未知环境的稀疏多视图手-物体重建