Sparse multi-view hand-object reconstruction for unseen environments

TL;DR

This paper introduces SVHO, a sparse multi-view method for reconstructing unseen hand-object interactions from RGB images, balancing accuracy and generalization without multi-view optimization.

Contribution

The paper presents a novel sparse multi-view reconstruction model that effectively handles unseen objects and hands without requiring optimization across views.

Findings

Additional views improve reconstruction quality.

Model trained on synthetic data generalizes to real-world data.

Sparse multi-view approach balances accuracy and computational efficiency.

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.