GAMES Webinar 2020 – 157期(绘制专题) | Lifan Wu(NVIDIA), Yazhen Yuan(SenseTime)
【GAMES Webinar 2020-157期】(绘制专题)
报告嘉宾1： Lifan Wu(NVIDIA)
报告时间：2020年9月24日上午10:00-10:45（北京时间）
报告题目：Analytic Spherical Harmonic Gradients for Real-Time Rendering With Many Polygonal Area Lights
报告摘要：
Recent work has developed analytic formulae for spherical harmonic (SH) coefficients from uniform polygonal lights, enabling near-field area lights to be included in Precomputed Radiance Transfer (PRT) systems, and in offline rendering. However, the method is inefficient since coefficients need to be recomputed at each vertex or shading point, for each light, even though the SH coefficients vary smoothly in space. The complexity scales linearly with the number of lights, making many-light rendering difficult. In this paper, we develop a novel analytic formula for the spatial gradients of the spherical harmonic coefficients for uniform polygonal area lights. The result is a significant generalization, involving the Reynolds transport theorem to reduce the problem to a boundary integral for which we derive a new analytic formula, showing how to reduce a key term to an earlier recurrence for SH coefficients. The implementation requires only minor additions to existing code for SH coefficients. The results also hold implications for recent efforts on differentiable rendering. We show that SH gradients enable very sparse spatial sampling, followed by accurate Hermite interpolation. This enables scaling PRT to hundreds of area lights} with minimal overhead and real-time frame rates. Moreover, the SH gradient formula is a new mathematical result that potentially enables many other graphics applications.
讲者简介：
Lifan Wu is a research scientist at NVIDIA. He completed his Ph.D. from University of California San Diego in 2020, advised by Prof. Ravi Ramamoorthi. He received his Bachelor’s degree in Computer Science from Tsinghua University in 2015. His research interest is physically-based rendering, including realistic appearance modeling, differentiable light transport, as well as sparse sampling and reconstruction of visual signals. He received the NVIDIA Graduate Fellowship in 2019.
讲者个人主页: https://winmad.github.io
报告嘉宾2： Yazhen Yuan(SenseTime)
报告时间：2020年9月24日上午10:45-11:30（北京时间）
报告题目：Tile Pair-Based Adaptive Multi-Rate Stereo Shading
报告摘要：
This work proposes a new stereo shading architecture that enables adaptive shading rates and automatic shading reuse among triangles and between two views. The proposed pipeline presents several novel features. First, the present sort-middle/bin shading is extended to tile pair-based shading to rasterize and shade pixels at two views simultaneously. A new rasterization algorithm utilizing epipolar geometry is then proposed to schedule tile pairs and perform rasterization at stereo views efficiently. Second, this work presents an adaptive multi-rate shading framework to compute shading on pixels at different rates. A novel tile-based screen space cache and a new cache reuse shader are proposed to perform such multi-rate shading across triangles and views. The results show that the newly proposed method outperforms the standard sort-middle shading and the state-of-the-art multi-rate shading by achieving considerably lower shading cost and memory bandwidth.
讲者简介：
讲者个人主页: https://scholar.google.com/citations?hl=en&user=4EhxgBkAAAAJ
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