Gradient-domain rendering methods can render higher-quality images at the same time cost compared with traditional ray tracing rendering methods, and, combined with the neural network, achieve better rendering quality...Gradient-domain rendering methods can render higher-quality images at the same time cost compared with traditional ray tracing rendering methods, and, combined with the neural network, achieve better rendering quality than conventional screened Poisson reconstruction. However, it is still challenging for these methods to keep detailed information, especially in areas with complex indirect illumination and shadows. We propose an unsupervised reconstruction method that separates the direct rendering from the indirect, and feeds them into our unsupervised network with some corresponding auxiliary channels as two separated tasks. In addition, we introduce attention modules into our network which can further improve details. We finally combine the results of the direct and indirect illumination tasks to form the rendering results. Experiments show that our method significantly improves image quality details, especially in scenes with complex conditions.展开更多
Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test ...Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.展开更多
基金supported by the National Key Research and Development Program of China under Grant No.2020YFB1708900the National Natural Science Foundation of China under Grant No.62272275.
文摘Gradient-domain rendering methods can render higher-quality images at the same time cost compared with traditional ray tracing rendering methods, and, combined with the neural network, achieve better rendering quality than conventional screened Poisson reconstruction. However, it is still challenging for these methods to keep detailed information, especially in areas with complex indirect illumination and shadows. We propose an unsupervised reconstruction method that separates the direct rendering from the indirect, and feeds them into our unsupervised network with some corresponding auxiliary channels as two separated tasks. In addition, we introduce attention modules into our network which can further improve details. We finally combine the results of the direct and indirect illumination tasks to form the rendering results. Experiments show that our method significantly improves image quality details, especially in scenes with complex conditions.
基金supported by the National Key R&D Program of China(No.2016YFB1200203)the National Natural Science Foundation of China(Nos.41427806 and 61273233)
文摘Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.
