Memory-Aware and Efficient Ray-Casting Algorithm

Ray-casting implementations require that the connectivity between the cells of the dataset to be explicitly computed and kept in memory. This constitutes a huge obstacle for obtaining real-time rendering for very large models. In this paper, we address this problem by introducing a new implementation of the ray-casting algorithm for irregular datasets. Our implementation optimizes the memory usage of past implementations by exploring ray coherence. The idea is to keep in main memory the information of the faces traversed by the ray cast through every pixel under the projection of a visible face. Our results show that exploring pixel coherence reduces considerably the memory usage, while keeping the performance of our algorithm competitive with the fastest previous ones.