The question of what are good views of a 3D object has been addressed by numerous researchers in perception, computer vision, and computer graphics. This has led to a large variety of measures for the goodness of views as well as some special-case viewpoint selection algorithms. In this article, we leverage the results of a large user study to optimize the parameters of a general model for viewpoint goodness, such that the fitted model can predict people's preferred views for a broad range of objects. Our model is represented as a combination of attributes known to be important for view selection, such as projected model area and silhouette length. Moreover, this framework can easily incorporate new attributes in the future, based on the data from our existing study. We demonstrate our combined goodness measure in a number of applications, such as automatically selecting a good set of representative views, optimizing camera orbits to pass through good views and avoid bad views, and trackball controls that gently guide the viewer towards better views.
InVEST is an interactive and visual tool for constructingevolutionarytrees from an ordered list of edges. In this paper it is shown that many methods for constructing evolutionary trees reduce to the edge selection problem. Furthermore, through a simulation study, it is shown that noninteractive methods for edge selection often perform poorly and can conceal alternative solutions. InVEST allows the user to interact with and explore an ordered list of edges facilitating the incorporation of user domain knowledge into the evolutionary tree construction process. a The RDP's URL is http: www.cme.msu.edu RDP index.html
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.