The Reyes image rendering architecture
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
Stochastic rasterization using time-continuous triangles
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Practical logarithmic rasterization for low-error shadow maps
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Efficient depth buffer compression
GH '06 Proceedings of the 21st ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Data-parallel rasterization of micropolygons with defocus and motion blur
Proceedings of the Conference on High Performance Graphics 2009
Hardware implementation of micropolygon rasterization with motion and defocus blur
Proceedings of the Conference on High Performance Graphics
Analytical motion blur rasterization with compression
Proceedings of the Conference on High Performance Graphics
Real-time stochastic rasterization on conventional GPU architectures
Proceedings of the Conference on High Performance Graphics
Adaptive volumetric shadow maps
EGSR'10 Proceedings of the 21st Eurographics conference on Rendering
Design and novel uses of higher-dimensional rasterization
EGGH-HPG'12 Proceedings of the Fourth ACM SIGGRAPH / Eurographics conference on High-Performance Graphics
A sort-based deferred shading architecture for decoupled sampling
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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Previous depth buffer compression schemes are tuned for compressing depths values generated when rasterizing static triangles. They provide generous bandwidth usage savings, and are of great importance to graphics processors. However, stochastic rasterization for motion blur and depth of field is becoming a reality even for real-time graphics, and previous depth buffer compression algorithms fail to compress such buffers due to the irregularity of the positions and depths of the rendered samples. Therefore, we present a new algorithm that targets compression of scenes rendered with stochastic motion blur rasterization. If possible, our algorithm fits a single time-dependent predictor function for all the samples in a tile. However, sometimes the depths are localized in more than one layer, and we therefore apply a clustering algorithm to split the tile of samples into two layers. One time-dependent predictor function is then created per layer. The residuals between the predictor and the actual depths are then stored as delta corrections. For scenes with moderate motion, our algorithm can compress down to 65% compared to 75% for the previously best algorithm for stochastic buffers.