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)


报告题目:Analytic Spherical Harmonic Gradients for Real-Time Rendering With Many Polygonal Area Lights


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.


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.


报告嘉宾2: Yazhen Yuan(SenseTime)


报告题目:Tile Pair-Based Adaptive Multi-Rate Stereo Shading


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.


Yazhen Yuan is a research engineer in SenseTime HangZhou, where he works on AR rendering and digital avatar. Yuan received his PHD degree from State Key Laboratory of CAD&CG, Zhejiang University in 2018 under the supervision of Prof. Hujun Bao and Prof. Rui Wang. His interests are mainly in the optimization framework for real-time rendering, especially shader simplification, mesh simplification and multi-rate shading. Most of his works can be found in



徐昆,清华大学计算机系教研系列副教授,博士生导师。2009年毕业于清华大学获博士学位。研究方向为计算机图形学,主要从事真实感绘制、可视媒体内容编辑与生成等方面的研究。发表SCI论文20余篇,其中12篇论文发表在ACM TOG, IEEE TVCG等重要期刊和会议上。曾获国家自然科学奖二等奖(排名第4),中国计算机学会优秀博士学位论文奖,入选中国科协“青年人才托举工程”。讲者个人主页:

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