Context and Feature Sensitive Re-sampling from Discrete Surface Measurements

This paper concerns context and feature-sensitive re-sampling of workspace surfaces represented by 3D point clouds. We interpret a point cloud as the outcome of repetitive and non-uniform sampling of the surfaces in the workspace. The nature of this sampling may not be ideal for all applications, representations and downstream processing. For example it might be preferable to have a high point density around sharp edges or near marked changes in texture. Additionally such preferences might be dependent on the semantic classification of the surface in question. This paper addresses this issue and provides a framework which given a raw point cloud as input, produces a new point cloud by re-sampling from the underlying workspace surfaces. Moreover it does this in a manner which can be biased by local low-level geometric or appearance properties and higher level (semantic) classification of the surface. We are in no way prescriptive about what justifies a biasing in the re-sampling scheme — this is left up to the user who may encapsulate what constitutes “interesting” into one or more “policies” which are used to modulate the default re-sampling behavior.