terça-feira, 8 de janeiro de 2013

2009b - Learning good views through intelligent galleries

Abstract. The definition of a good view of a 3D scene is highly subjective and strongly depends on both the scene
content and the 3D application. Usually, camera placement is performed directly by the user, and that task may
be laborious. Existing automatic virtual cameras guide the user by optimizing a single rule, e.g. maximizing the
visible silhouette or the projected area. However, the use of a static pre-defined rule may fail in respecting the
user’s subjective understanding of the scene. This work introduces intelligent design galleries, a learning approach
for subjective problems such as the camera placement. The interaction of the user with a design gallery teaches
a statistical learning machine. The trained machine can then imitate the user, either by pre-selecting good views
or by automatically placing the camera. The learning process relies on a Support Vector Machines for classifying
views from a collection of descriptors, ranging from 2D image quality to 3D features visibility. Experiments of the
automatic camera placement demonstrate that the proposed technique is efficient and handles scenes with occlusion
and high depth complexities. This work also includes user validations of the intelligent gallery interface.
Keywords: Learning. Camera Positioning. Virtual Camera. Intelligent Camera. Good View.

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