SHOW3D: Capturing Scenes of 3D Hands and Objects in the Wild
This addresses the problem of limited generalization in hand-object interaction datasets for egocentric computer vision by providing a large-scale, in-the-wild dataset, though it is incremental in improving data collection methods.
The paper tackles the challenge of 3D understanding of hands and objects in real-world scenarios by introducing a marker-less multi-camera system that captures diverse environments, resulting in the SHOW3D dataset with precise 3D annotations validated on downstream tasks.
Accurate 3D understanding of human hands and objects during manipulation remains a significant challenge for egocentric computer vision. Existing hand-object interaction datasets are predominantly captured in controlled studio settings, which limits both environmental diversity and the ability of models trained on such data to generalize to real-world scenarios. To address this challenge, we introduce a novel marker-less multi-camera system that allows for nearly unconstrained mobility in genuinely in-the-wild conditions, while still having the ability to generate precise 3D annotations of hands and objects. The capture system consists of a lightweight, back-mounted, multi-camera rig that is synchronized and calibrated with a user-worn VR headset. For 3D ground-truth annotation of hands and objects, we develop an ego-exo tracking pipeline and rigorously evaluate its quality. Finally, we present SHOW3D, the first large-scale dataset with 3D annotations that show hands interacting with objects in diverse real-world environments, including outdoor settings. Our approach significantly reduces the fundamental trade-off between environmental realism and accuracy of 3D annotations, which we validate with experiments on several downstream tasks. show3d-dataset.github.io