Hybrid transparency is an approach for real-time approximation of order-independent transparency. Our hybrid approach combines an accurate compositing, of a few core transparent layers, with a quick approximation, for the remaining layers. Its main advantage, the ability to operate in bounded memory without noticeable artifacts, enables its usage with high scene complexity and image resolution, which other approaches fail to handle. Hybrid transparency is suitable for highly-parallel execution, can be implemented in current GPUs and further improved, with minimal architecture changes. We present quality, memory, and performance analysis and comparisons which demonstrate that hybrid transparency is able to generate high-quality images at competitive frames rates and with the lowest memory consumption among comparable OIT techniques.

Path planning is an active topic in the literature, and efficient navigation over non-planar surfaces is an open research question. In this work we present a novel technique for navigation of multiple agents over arbitrary triangular domains. The proposed solution uses a fast hierarchical computation of geodesic distances over triangular meshes to allow interactive frame rates, and a GPU-based collision avoidance technique to guide individual agents. Unlike most previous work, the method imposes no limitations on the surface over which the agents are moving, and can naturally deal with non-planar meshes of arbitrary genus and curvature. Moreover, the implementation is a hybrid CPU/GPU algorithm that explores the current trend of increasing the number of CPU cores and GPU programmability. This approach exploits the best qualities in each processor, thus achieving very high performance.

Abstract There is a vast number of applications that require distance field computation over triangular meshes. State‐of‐the‐art algorithms have quadratic or sub‐quadratic worst‐case complexity, making them impractical for interactive applications. While most of the research on this subject has been focused on reducing the computation complexity of the algorithms, in this work we propose an approximate algorithm that achieves similar results working in lower resolutions of the input meshes. The creation of lower resolution meshes is the essence of our proposal. The idea is to identify regions on the input mesh that can be unfolded into planar regions with minimal area distortion (i.e. quasi‐developable charts). Once charts are computed, their interior is re‐triangulated to reduce the number of triangles, which results in a collection of simplified charts that we call a base mesh. Due to the properties of quasi‐developable regions, we are able to compute distance fields over the base mesh instead of over the input mesh. This reduces the memory footprint and data processed for distance computations, which is the bottleneck of these algorithms. We present results that are one order of magnitude faster than current exact solutions, with low approximation errors.