.png){kind=icon}
.png){kind=icon}
Return
Automatic Shader Simplification Using Surface Signal Approximation
.png){kind=icon}
PRE
AI
DOI：10.1145/2661229.2661276.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we present a new automatic shader simplification method using surface signal approximation. We regard the entire multi-stage rendering pipeline as a process that generates signals on surfaces, and we formulate the simplification of the fragment shader as a global simplification problem across multi-shader stages. Three new shader simplification rules are proposed to solve the problem. First, the code transformation rule transforms fragment shader code to other shader stages in order to redistribute computations on pixels up to the level of geometry primitives. Second, the surface-wise approximation rule uses high-order polynomial basis functions on surfaces to approximate pixel-wise computations in the fragment shader. These approximations are pre-cached and simplify computations at runtime. Third, the surface subdivision rule tessellates surfaces into smaller patches. It combines with the previous two rules to approximate pixel-wise signals at different levels of tessellations with different computation times and visual errors. To evaluate simplified shaders using these simplification rules, we introduce a new cost model that includes the visual quality, rendering time and memory consumption. With these simplification rules and the cost model, we present an integrated shader simplification algorithm that is capable of automatically generating variants of simplified shaders and selecting a sequence of preferable shaders. Results show that the sequence of selected simplified shaders balance performance, accuracy and memory consumption well.
Keywords:
GPU shader
real-time rendering
shader simplification
surface signal approximation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.5
Papers:
4.7K
Citations:
3.6W