While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. It is also shown how this algorithm can be adopted to saliency detection in point clouds. Many applications can utilize these regions. In this paper we explore one such application-viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.

While saliency in images has been extensively studied in recent years, there is very little work on saliency of point sets. This is despite the fact that point sets and range data are becoming ever more widespread and have myriad applications. In this paper we present an algorithm for detecting the salient points in unorganized 3D point sets. Our algorithm is designed to cope with extremely large sets, which may contain tens of millions of points. Such data is typical of urban scenes, which have recently become commonly available on the web. No previous work has handled such data. For general data sets, we show that our results are competitive with those of saliency detection of surfaces, although we do not have any connectivity information. We demonstrate the utility of our algorithm in two applications: producing a set of the most informative viewpoints and suggesting an informative city tour given a city scan.

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. Many applications can utilize these regions. In this paper we explore one such application - viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.