Computing Saliency Map from Spatial Information in Point Cloud Data

Saliency detection in 2D and 3D images has been extensively used in many computer vision applications such as obstacle detection, object recognition and segmentation. In this paper we present a new saliency detection method which exploits the spatial irregularities in an environment. A Time-of-Flight (TOF) camera is used to obtain 3D points that represent the available spatial information in an environment. Two separate saliency maps are calculated by employing local surface properties (LSP) in different scales and the distance between the points and the camera. Initially, residuals representing the irregularities are obtained by fitting planar patches to the 3D points in different scales. Then, residuals within the spatial scales are combined and a saliency map in which the points with high residual values represent non-trivial regions of the surfaces is generated. Also, another saliency map is generated by using the proximity of each point in the point cloud data. Finally, two saliency maps are integrated by averaging and a master saliency map is generated.