Rendering on a Budget: a Framework for Time-Critical Rendering

Abstract We present a technique for optimizing the rendering of highdepth complexity scenes. Prioritized-Layered Projection (PLP) does this by rendering an estimation of the visible set for each frame. The novelty in our work lies in the fact that we do not explicitly compute visible sets. Instead, our work is based on computing on demand a priority order for the polygons that maximizes the likelihood of rendering visible polygons before occluded ones for any given scene. Given a fixed budget, e.g. time or number of triangles, our rendering algorithm makes sure to render geometry respecting the computed priority. There are two main steps to our technique: (1) an occupancy-based tessellation of space; and (2) a soliditybased traversal algorithm. PLP works by first computing an occupancy-based tessellation of space, which tends to have more cells where there are more geometric primitives. In this spatial tessellation, each cell is assigned a solidity value, which is directly proportional to its likelihood of occluding other cells. In its simplest form, a cell's solidity value is directly proportional to the number of polygons contained within it. During our traversal algorithm cells are marked for projection, and the geometric primitives contained within them actually rendered. The traversal algorithm makes use of the cells' solidity, and other view-dependent information to determine the ordering in which to project cells. By carefully tailoring the traversal algorithm to the occupancy-based tessellation, we can achieve very good frame rates with low preprocessing and rendering costs. In this paper, we describe our technique and its implementation in detail. Also, we provide experimental evidence of its performance. We also briefly discuss extensions of our algorithm

Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs

Deformable image registration in the presence of considerable contrast differences and large size and shape changes presents significant research challenges. First, it requires a robust registration framework that does not depend on intensity measurements and can handle large nonlinear shape variations. Second, it involves the expensive computation of nonlinear deformations with high degrees of freedom. Often it takes a significant amount of computation time and thus becomes infeasible for practical purposes. In this paper, we present a solution based on two key ideas: a new registration method that generates a mapping between anatomies represented as a multicompartment model of class posterior images and geometries and an implementation of the algorithm using particle mesh approximation on Graphical Processing Units (GPUs) to fulfill the computational requirements. We show results on the registrations of neonatal to 2-year old infant MRIs. Quantitative validation demonstrates that our proposed method generates registrations that better maintain the consistency of anatomical structures over time and provides transformations that better preserve structures undergoing large deformations than transformations obtained by standard intensity-only registration. We also achieve the speedup of three orders of magnitudes compared to a CPU reference implementation, making it possible to use the technique in time-critical applications