![]() ![]() Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. International Journal of Robotics Research 10, 628–649 (1991) 495–502 (2005)īarraquand, J., Latombe, J.C.: Robot motion planning: A distributed representation approach. ![]() ![]() Takahashi, S., Fujishiro, I., Takeshima, Y., Nishita, T.: A feature-driven approach to locating optimal viewpoints for volume visualization. ACM Transactions on Graphics 24, 659–666 (2005)īordoloi, U., Shen, H.W.: View selection for volume rendering. Lee, C.H., Varshney, A., Jacobs, D.: Mesh saliency. Vázquez, P., Feixas, M., Sbert, M., Heidrich, W.: Automatic view selection using viewpoint entropy and its application to image-based modelling. In: International Symposium on Visual Computing, pp. This process is experimental and the keywords may be updated as the learning algorithm improves.Ĭhan, M.Y., Qu, H., Wu, Y., Zhou, H.: Viewpoint selection for angiographic volume. These keywords were added by machine and not by the authors. Experiments on volumetric datasets have been conducted for demonstration. This path planning method can deliver useful guidance for volume exploration. To connect the viewpoints in a proper manner, potential fields are established using the projection maps and camera paths are derived accordingly. Good candidate viewpoints selected are covered in the path. Based on a novel propagation framework, we develop a feature and quality driven approach for viewpoint selection and camera path construction. To improve the visualization process, we propose a novel method for systematic revelation and illustration of the volume by presenting the data in a visual trail. Volume visualization is an effective means for exploring information in volumetric data. ![]()
0 Comments
Leave a Reply. |