Efficient Acquisition and Clustering of Local Histograms for Representing Voxel Neighborhoods

Abstract

In the past years many interactive volume rendering techniques have been proposed, which exploit the neighboring environment of a voxel during rendering. In general on-the-fly acquisition of this environment is infeasible due to the high amount of data to be taken into account. To bypass this problem we propose a GPU preprocessing pipeline which allows to acquire and compress the neighborhood information for each voxel. Therefore, we represent the environment around each voxel by generating a local histogram (LH) of the surrounding voxel densities. By performing a vector quantization (VQ), the high number of LHs is than reduced to a few hundred cluster centroids, which are accessed through an index volume. To accelerate the required computational expensive processing steps, we take advantage of the highly parallel nature of this task and realize it using CUDA. For the LH compression we use an optimized hybrid CPU/GPU implementation of the k-means VQ algorithm. While the assignment of each LH to its nearest centroid is done on the GPU using CUDA, centroid recalculation after each iteration is done on the CPU. Our results demonstrate the applicability of the precomputed data, while the performance is increased by a factor of about 10 compared to previous approaches.