Sparse Multi-view Hand-object Reconstruction for Unseen Environments

Yik Lung Pang, Changjae Oh, Andrea Cavallaro; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 803-810

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.