SALIENCY OF SUBTLE ENTITIES WITHIN 3-D POINT CLOUDS
Visual saliency is defined by regions of the scene that stand out from their neighbors and attract immediate attention. In image processing, visual saliency is frequently used to focus local analysis of key features. Though their advantage is largely acknowledged, little research has been carried concerning 3-D data, and even less in relation to data acquired by laser scanners for mapping. In this paper, we propose a new saliency measure for laser scanned point-clouds, governed by the neurological concepts of center-surround and low-level features. Adjusted to large point sets, we propose a fast geometric descriptor, which quantifies the distance of a point from its surrounding. We show that the proposed model highlights not only salient details in watertight models, but also in airborne and terrestrially scanned scenes that may hold subtle entities embedded within the topography. The detection of such regions paves the way to a myriad of applications, such as feature and pattern extraction, registration, classification, viewpoint selection, point-cloud simplification, landmark detection, etc.