Volume visualization for out-of-core 3D images based on semi-adaptive partitioning

Volume rendering techniques have been used widely for high quality visualization of 3D datasets, especially 3D images. However, when rendering very large (out-of-core) datasets, some traditional in-core volume rendering algorithms do not work due to the impossibility of fitting the entire input data in the main memory of a computer. Their simple out-of-core versions do not perform well either because of the slow speed external memory access overhead. In order to solve this problem, a semi-adaptive partitioning strategy and an efficient out-of-core volume rendering method based on it are proposed in this paper. By this new partitioning strategy, the out-of-core dataset is divided into small sub-blocks in different sizes, which are organized by a BSP tree. Each sub-block can be loaded into the fast texture memory of the graphics hardware and be rendered by certain volume rendering algorithm based on 3D texture. Then the final result is obtained by composing the projection images of all the sub-blocks from back to front after traveling the BSP tree according to the viewpoint position. The experimental results indicate that the new method is effective and efficient for the volume visualization of out-of-core 3D images.