Stream compaction is a common parallel primitive used to remove unwanted elements in sparse data. This allows highly parallel algorithms to maintain performance over several processing steps and reduces overall memory usage.For wide SIMD many-core architectures, we present a novel stream compaction algorithm and explore several variations thereof. Our algorithm is designed to maximize concurrent execution, with minimal use of synchronization. Bandwidth and auxiliary storage requirements are reduced significantly, which allows for substantially better performance.We have tested our algorithms using CUDA on a PC with an NVIDIA GeForce GTX280 GPU. On this hardware, our reference implementation provides a 3× speedup over previous published algorithms.
Figure 1: Our system captures a live performance in the infrared (IR) spectrum (bottom left). A target appearance (top left) is rendered and saved as albedo and offsets based on expression and spatial position (top middle). These are blended and deformed to match the facial configuration and position (bottom middle), and projected back on the performer for appearance augmentation (right). The average system latency is 9.8ms, achieved through GPU optimizations, and is further compensated for through prediction. AbstractWe propose the first system for live dynamic augmentation of human faces. Using projector-based illumination, we alter the appearance of human performers during novel performances. The key challenge of live augmentation is latency -an image is generated according to a specific pose, but is displayed on a different facial configuration by the time it is projected. Therefore, our system aims at reducing latency during every step of the process, from capture, through processing, to projection. Using infrared illumination, an optically and computationally aligned high-speed camera detects facial orientation as well as expression. The estimated expression blendshapes are mapped onto a lower dimensional space, and the facial motion and non-rigid deformation are estimated, smoothed and predicted through adaptive Kalman filtering. Finally, the desired appearance is generated interpolating precomputed offset textures according to time, global position, and expression. We have evaluated our system through an optimized CPU and GPU prototype, and demonstrated successful low latency augmentation for different performers and performances with varying facial play and motion speed. In contrast to existing methods, the presented system is the first method which fully supports dynamic facial projection mapping without the requirement of any physical tracking markers and incorporates facial expressions. object or a person would look like when painted or altered in different ways, both statically and dynamically. For physical objects, this can be done with the aid of projection-based illumination, bringing the flexibly of digital objects to physical appearance [RWF99, BRF01, BBG * 13, AIS15, NWI15, ZXT * 16]. Since introduced, these methods have been used extensively in education, training, entertainment and sales.
We investigate the use of two-level nested grids as acceleration structure for ray tracing of dynamic scenes. We propose a massively parallel, sort-based construction algorithm and show that the two-level grid is one of the structures that is fastest to construct on modern graphics processors. The structure handles non-uniform primitive distributions more robustly than the uniform grid and its traversal performance is comparable to those of other high quality acceleration structures used for dynamic scenes. We propose a cost model to determine the grid resolution and improve SIMD utilization during ray-triangle intersection by employing a hybrid packetization strategy. The build times and ray traversal acceleration provide overall rendering performance superior to previous approaches for real time rendering of animated scenes on GPUs.
Interactive rendering of large scale vector maps is a key challenge for high-quality geographic visualization software systems. In this paper we present a novel approach for the visualization of large scale vector maps over detailed height-field terrains. Our method uses a deferred line shading approach to render large scale vector maps directly in a screen-space shading stage over a terrain visualization. The fact that there is no traditional geometric polygonal rendering involved allows our algorithm to outperform conventional vector map rendering algorithms for geographic information systems. Our flexible clustered deferred line rendering approach allows a user to interactively customize and apply advanced vector styling methods, as well as the integration into a vector map level-of-detail system. AbstractInteractive rendering of large scale vector maps is a key challenge for high-quality geographic visualization software systems. In this paper we present a novel approach for the visualization of large scale vector maps over detailed height-field terrains. Our method uses a deferred line shading approach to render large scale vector maps directly in a screen-space shading stage over a terrain visualization. The fact that there is no traditional geometric polygonal rendering involved allows our algorithm to outperform conventional vector map rendering algorithms for geographic information systems. Our flexible clustered deferred line rendering approach allows a user to interactively customize and apply advanced vector styling methods, as well as the integration into a vector map levelof-detail system.
Voxels are a popular choice to encode complex geometry. Their regularity makes updates easy and enables random retrieval of values. The main limitation lies in the poor scaling with respect to resolution. Sparse voxel DAGs (Directed Acyclic Graphs) overcome this hurdle and offer high‐resolution representations for real‐time rendering but only handle static data. We introduce a novel data structure to enable interactive modifications of such compressed voxel geometry without requiring de‐ and recompression. Besides binary data to encode geometry, it also supports compressed attributes (e.g., color). We illustrate the usefulness of our representation via an interactive large‐scale voxel editor (supporting carving, filling, copying, and painting).
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