Consistent segment-wise matching with multi-layer graphs

Segment-wise matching is an important problem for higher-level understanding of shapes and geometry analysis. Many existing segment-wise matching techniques assume perfect segmentation, and would suffer from imperfect or over-segmentation inputs. To handle this shortcoming, we propose a multi-layer graph (MLG) to represent possible partially merged segments of input shape. We adapt the diffusion pruning technique on the MLGs to find high quality segment-wise matching. Experimental results on man-made shapes demonstrate the effectiveness of our method

Instance-based learning of affordances

The discovery of possible interactions with objects is a vital part of an exploration task for robots. An important subset of these possible interactions are affordances. Affordances describe what a specific object can afford to a specific agent, based on the capabilities of the agent and the properties of the object in relation to the agent. For example, a chair affords a human to be sat-upon, if the sitting area of the chair is approximately knee-high. In this work, an instance-based learning approach is made to discover these affordances solely through different visual representations of point cloud data of an object. The point clouds are acquired with a Microsoft Kinect sensor. Different representations are tested and evaluated against a set of point cloud data of various objects found in a living room environment