3 accepted papers
Understanding hand-object interaction (HOI) is fundamental to computer vision, robotics, and AR/VR. However, conventional hand videos often lack essential physical information, such as contact forces and motion dynamics, and are prone to frequent occlusions. To address these challenges, we present G
This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation, i.e., point clouds, involves discrete samples of the underlying continuous 3D surface. This discretization process intro…
Efficient 3D space sampling to represent an underlying3D object/scene is essential for 3D vision, robotics, and be-yond. A standard approach is to explicitly sample a densecollection of views and formulate it as a view selection prob-lem, or, more generally, a set cover problem. In this paper,we int…