Recently, sort-middle triangle rasterization, implemented as software on a manycore GPU with vector units (Larabee), has been proposed as an alternative to hardware rasterization. The main reasoning is, that only a fraction of the time per frame is spent sorting and rasterizing triangles. However is this still a valid argument in the context of geometry amplification when the number of primitives increases quickly? A REYES like approach, sorting parametric patches instead, could avoid many of the problems with tiny triangles. To demonstrate that software rasterization with geometry amplification can work in real-time, we implement a tile based sort-middle rasterizer in CUDA and analyze its behavior: First we adaptively subdivide rational bicubic B\u00b4ezier patches. Then we sort those into buckets, and for each bucket we dice, grid-shade, and rasterize the micropolygons into the corresponding tile using on-chip caches. Despite being limited by the amount of available shared memory, the number of registers and the lack of an L3 cache, we manage to rasterize 1600\u00d71200 images, containing 200k sub-patches, at 10-12 fps on an nVidia GTX 280. This is about 3x to 5x slower than a hybrid approach subdividing with CUDA and using the HW rasterizer. We hide cracks caused by adaptive subdivision using a flatness metric in combination with rasterizing B\u00b4ezier convex hulls. Using a k-buffer with a fuzzy z-test we can render transparent objects despite the overlap our approach creates. Further, we introduce the notion of true back patch culling, allowing us to simplify crack hiding and sampling.<\/p>\n","protected":false},"excerpt":{"rendered":"
Recently, sort-middle triangle rasterization, implemented as software on a manycore GPU with vector units (Larabee), has been proposed as an alternative to hardware rasterization. The main reasoning is, that only a fraction of the time per frame is spent sorting and rasterizing triangles. 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