Deep part induction from articulated object pairs

Articulated Object Recognition

Complex Articulated Object Tracking

In this paper new results are presented for tracking complex multi-body objects. The theoretical framework is based on robotics techniques and uses an a-priori model of the object including a general mechanical link description. A new kinematic-set formulation takes into account that articulated degrees of freedom are directly observable from the camera and therefore their estimation does not n...

Articulated Object Detection

We develop an object detection algorithm for difficult-to-detect objects with consumerlevel range cameras. The increasing popularity of such devices made the process of obtaining depth data extremely easy. The availability of depth data motivates the need for algorithms that will be able to process it in meaningful ways, so that it then can be used in applications like robot navigation or objec...

Articulated Object Reconstruction and Markerless Motion Capture from Depth Video

We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise-rigid object using a single depth video camera. The algorithm identifies and tracks the rigid components in each frame, while accumulating the geometric information acquired over time, possibly from different viewpoints. The algorithm also reconstructs the dynamic skeleton of the object, thus can be ...

Learning Articulated Object Models from Language and Vision

In order for robots to operate effectively in homes and workplaces, they must be able to manipulate the articulated objects common within environments built for and by humans. Kinematic models provide a concise representation of these objects that enable deliberate, generalizable manipulation policies. However, existing approaches to learning these models rely upon visual observations of an obj...