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Abstract
This paper is dedicated to virtual world exploration techniques, which have to help a human being to understand a 3D scene. A new method to compute a global view of a scene is presented in the paper. The global view of a scene is determined by a “good” set off points of view. This method is based on a genetic algorithm. The “good” set of points of view is used to compute a camera path around the scene.
Highlights
In the recent years, the concept of virtual world has evolved and become more and more important
We propose a new method which employs genetic algorithms in order to evolve a given set of viewpoints into another that offers a better view of the scene
The paper is organized in the following manner: in Section 2 we review related works, Section 3 details the definition of the quality of a set of viewpoints, Section 4 takes a closer look at how genetic algorithms are used in our implementation, Section 5 presents some of the results we achieved, and in Section 6 a conclusion is given with a brief description of future works
Summary
The concept of virtual world has evolved and become more and more important. The purpose of a virtual world exploration in computer graphics is to permit a human being, a user, to acquire enough information in order to better understand the environment he or she is faced with This is done by guiding a virtual camera using an automatically computed path, depending on the nature of the world. The trajectory of the virtual camera is usually obtained using a set of “good” points of view that are either predetermined or calculated during the actual movement This is directly connected to the notion of viewpoint quality, which plays an important role in path computation and in scene understanding. 2) Offline exploration, where the camera path is pre-computed in a preliminary step This means that the virtual world is found and analysed by the program guiding the camera, in order to determine interesting points of view and the paths linking these points. The paper is organized in the following manner: in Section 2 we review related works, Section 3 details the definition of the quality of a set of viewpoints, Section 4 takes a closer look at how genetic algorithms are used in our implementation, Section 5 presents some of the results we achieved, and in Section 6 a conclusion is given with a brief description of future works
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