Improving Efficiency for CUDA-based Volume Rendering by Combining Segmentation and Modified Sampling Strategies

The objective of this paper is to present a speed-up method to improve the rendering speed of ray casting at the same time obtaining high-quality images. Ray casting is the most commonly used volume rendering algorithm, and suitable for parallel processing. In order to improve the efficiency of parallel processing, the latest platform-Compute Unified Device Architecture (CUDA) is used. The speed-up method uses improved workload allocation and sampling strategies according to CUDA features. To implement this method, the optimal number of segments of each ray is dynamically selected based on the change of the corresponding visual angle, and each segment is processed by a distinct thread processor. In addition, for each segment, we apply different sampling quantity and density according to the distance weight. Rendering speed results show that our method achieves an average 70% improvement in terms of speed, and even 145% increase in some special cases, compared to conventional ray casting on Graphics Processing Unit (GPU). Speed-up ratio shows that this method can effectively improve the factors that influence efficiency of rendering. Excellent rendering performance makes this method contribute to real-time 3-D reconstruction

QuadStack: An Efficient Representation and Direct Rendering of Layered Datasets

We introduce QuadStack, a novel algorithm for volumetric data compression and direct rendering. Our algorithm exploits the data redundancy often found in layered datasets which are common in science and engineering fields such as geology, biology, mechanical engineering, medicine, etc. QuadStack first compresses the volumetric data into vertical stacks which are then compressed into a quadtree that identifies and represents the layered structures at the internal nodes. The associated data (color, material, density, etc.) and shape of these layer structures are decoupled and encoded independently, leading to high compression rates (4× to 54× of the original voxel model memory footprint in our experiments). We also introduce an algorithm for value retrieving from the QuadStack representation and we show that the access has logarithmic complexity. Because of the fast access, QuadStack is suitable for efficient data representation and direct rendering. We show that our GPU implementation performs comparably in speed with the state-of-the-art algorithms (18-79 MRays/s in our implementation), while maintaining a significantly smaller memory footprint