A case study on automatic camera placement and motion for visualizing historical data

Abstract

In this paper, we address the problem of automatic camera positioning and automatic camera path generation in the context of historical data visualization. After short description of the given data, we elaborate on the constraints for the positioning of a virtual camera in such a way that not only the projected area is maximized, but also the depth of the displayed scene. This is especially important when displaying terrain models, which do not provide good 3D impression when only the projected area is maximized. Based on this concept, we present a method for computing an optimal camera position for each instant of time.Since the explored data are not static, but change depending on the explored scene time, we also discuss a method for animation generation. In order to avoid sudden changes of the camera position, when the previous method is applied for each frame (point in time), we introduce pseudo-events in time, which expand the bounding box defined by the currently active events of interest. In particular, this technique allows events happening in a future point in time to be taken into account such that when this time becomes current, all events of interest are already within the current viewing frustum of the camera.

View Selection of 3D Objects Based on Saliency Segmentation

“…They classified these methods into five types according to the attributes of views used in measuring view goodness: (1) area, e.g. projected area, surface visibility [2] and viewpoint entropy [3,4]; (2) contour [5,6]; (3) depth [7,8]; (4) surface curvature [9][10][11][12][13]; (5) semantic [7,[14][15][16].…”

Mesh saliency

“…Kamada and Kawai [1988] describe a method for selecting views in which surfaces are imaged non-obliquely relative to their normals, using parallel projection. Stoev and Straßer [2002] consider a different approach that is more suitable to viewing terrains, in which most surface normals in the scene are similar, and visible scene depth should be maximized. In the context of computer vision, Weinshall and Werman [1997] show an equivalence between the most stable and most likely view of an object, and show that this is the view in which an object is flattest.…”

ViewCube

“…Researchers have also explored other camera metaphors including -arcball‖ orbiting [Shoemake 1992] and flying [Stoev and Straber 2002], using constraints [Mackinlay et al 1990;Singh and Balakrishnan 2004;Hanson and Wernet 1997], drawing a path [Igarashi et al 1998], through-the-lens control [Gleicher and Witkin 1992], points and areas of interests [Jul and Furnas 1998], two-handed techniques [Balakrishnan and Kurtenbach 1999;Zeleznik and Forsberg 1999], and combinations of techniques [Shneiderman 1983;Smith et al 2001;Ware and Osborne 1990]. Bowman et al present taxonomies and evaluations of various interactions and camera models [1999; 1997].…”