Scene understanding is central to general physical intelligence, and video is a primary modality for capturing both state and temporal dynamics of a scene. Yet understanding physical processes remains difficult, as models must combine object localization, hand-object interactions, relational …

Semantic scene understanding is crucial aspect of modern day robotic applications. With the recent advances in deep learning and the increased availability of large scale richly annotated datasets, popularity of 3D scene understanding tasks has increased rapidly. In this work, we present a …

In this work, we take VoteNet, a state-of-the-art deep neural network for object detection, as a basis and extend it for computing epistemic uncertainty in its predictions with Monte Carlo dropout. We show that this quantity can be used as a weighting factor for predictions, leading to an increase …