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Reconstruction of freeform surfaces from scattered coordinate data is a difficult problem encountered in many surface fitting and geometric modeling applications. Conventional tessellation and parametric surface fitting techniques are limited because they require prior knowledge about the connectivity between the sampled points. The method of surface reconstruction described in this paper exploits the learning capability of a self-organizing feature map (SOFM) to adaptively fit a deformable sphere to the unorganized 3D coordinate data. The learning algorithm automatically establishes the connectivity between the measured points by iteratively changing the topological relationships within the map. By incorporating additional constraints during the learning process it is possible to have the deformable map follow the shape of objects with surface holes and cavities. Several examples of freeform surfaces with varying levels of complexity are discussed in order to demonstrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Microsurgery is a highly complex surgical procedure on small body parts performed by a dedicated surgical team. An operating microscope is typically used to obtain a precise view of the soft tissues. The complexity of the microsurgical procedure makes it a suitable application of virtual/augmented reality technology for training purpose. In this paper, we present an overview of our simulator and then describe in details the visualization work that reconstructs the magnified view of the operating area from medical images. The visualization component is based entirely on our newly proposed dynamic linear level octree (DLLO)-based volume rendering methods. In comparison with the conventional ray casting algorithms, our computational method achieves fast rendering speed, reduces hardware requirement and combines surface-volume rendering for realistic and interactive PC-based medical simulations. The new algorithms are suitable for rendering dynamic medical data which are increasingly popular clinically. [ABSTRACT FROM AUTHOR]