The Experts below are selected from a list of 1581 Experts worldwide ranked by ideXlab platform
Sungeui Yoon - One of the best experts on this subject based on the ideXlab platform.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient Incident Radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce Incident Radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the Incident Radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irRadiance caching.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy input...
Yuchi Huo - One of the best experts on this subject based on the ideXlab platform.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient Incident Radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce Incident Radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the Incident Radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irRadiance caching.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy input...
Rui Wang - One of the best experts on this subject based on the ideXlab platform.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient Incident Radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce Incident Radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the Incident Radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irRadiance caching.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy input...
Hujun Bao - One of the best experts on this subject based on the ideXlab platform.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient Incident Radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce Incident Radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the Incident Radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irRadiance caching.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy input...
Ruzahng Zheng - One of the best experts on this subject based on the ideXlab platform.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient Incident Radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce Incident Radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the Incident Radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irRadiance caching.
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adaptive Incident Radiance field sampling and reconstruction using deep reinforcement learning
ACM Transactions on Graphics, 2020Co-Authors: Yuchi Huo, Rui Wang, Ruzahng Zheng, Hujun Bao, Sungeui YoonAbstract:Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy input...
