Volume rendering by adaptive refinement
The Visual Computer: International Journal of Computer Graphics
Footprint evaluation for volume rendering
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Hierarchical splatting: a progressive refinement algorithm for volume rendering
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Fast algorithms for volume ray tracing
VVS '92 Proceedings of the 1992 workshop on Volume visualization
Frequency domain volume rendering
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
ACM Transactions on Graphics (TOG)
Accelerated volume rendering and tomographic reconstruction using texture mapping hardware
VVS '94 Proceedings of the 1994 symposium on Volume visualization
Efficiently using graphics hardware in volume rendering applications
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Frequency domain volume rendering by the wavelet X-ray transform
IEEE Transactions on Image Processing
Sorting-free pre-integrated projected tetrahedra
Proceedings of the 2009 Workshop on Ultrascale Visualization
Level-of-detail rendering of large-scale irregular volume datasets using particles
Journal of Computer Science and Technology
High-quality particle-based volume rendering for large-scale unstructured volume datasets
Journal of Visualization
| Hi-index | 0.00 |
Although Monte Carlo Volume Rendering (MCVR) is an efficient point-based technique for generating simulated X-ray images from large CT data, its practical application in medical imaging systems is limited by the relatively expensive preprocessing. The quality of images is strongly influenced by the transfer function, which maps a data value onto a sampling probability. An appropriate transfer function concentrates the point samples onto the region of interest. Since it is data dependent, a fine parameter tuning is necessary. However, the costly preprocessing has to be repeated whenever the transfer function parameters are modified. In this paper a new preprocessing algorithm is proposed for MCVR, which allows for an interactive transfer function control in the rendering phase, providing a visual feedback in a couple of seconds. In order to rapidly recompute point samples according to the modified transfer function, an efficient hybrid sampling strategy is applied, which combines the advantages of the probabilistic Monte Carlo sampling and the deterministic quasi-Monte Carlo sampling.