Abstract
Abstract. 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.
Highlights
Visual saliency is defined by regions of the scene that stand out from their neighbors and attract immediate attention
Within 3-D point-clouds, visual attention can be harnessed to reduce the problem of scene understanding into rapid series of less demanding computational procedures, aimed to support localized visual analysis problems, e.g., detection, simplification, registration and others
Multi-scale image features were combined into a single topographical saliency map. van de Weijer et al (2006) focused on color distinctness by estimating the probab
Summary
Visual saliency is defined by regions of the scene that stand out from their neighbors and attract immediate attention. Most approaches proposed to use the directional changes between a point and its neighbors, described by fast point feature histogram (FPFH; Rusu et al, 2009) as the main contributor to saliency This is achieved by a hierarchical model that measures the similarity and dissimilarity by the distances between the points’ FPFH (Shtrom et al, 2013; Tasse et al, 2015; Kobyshev et al, 2016; Ding et al, 2019). We propose a new saliency measure which is adapted to open scenes and is governed by surface geometry, while maintaining the neurological concepts of center-surround and low-level features. The new saliency can be further integrated as preliminary step for local analysis of key entities for object extraction, classification, registration, smart down-sampling, etc
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