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| 1 | A survey on deep learning-based Monte Carlo denoising显示文摘Monte Carlo(MC)integration is used ubiquitously in realistic image synthesis because of its flexibility and generality.However,the integration has to balance estimator bias and variance,which causes visually distracting noise with low sample counts.Existing solutions fall into two categories,in-process sampling schemes and post-processing reconstruction schemes.This report summarizes recent trends in the post-processing reconstruction scheme.Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning,by training neural networks to reconstruct denoised rendering results from sparse MC samples.Many of these techniques show promising results in real-world applications,and this report aims to provide an assessment of these approaches for practitioners and researchers. | Yuchi Huo Sung-eui Yoon | 2021 | Computational Visual Media2021，7，2： | 5 |
| 2 | Wind resistance aerial path planning for efficient reconstruction of offshore ship显示文摘When the unmanned aerial vehicle(UAV)is applied to three-dimensional(3D)reconstruction of the offshore ship,it faces two problems:the battery capacity limitation of the UAV and the disturbance of the wind in the environment.Wind disturbance is generally not considered in the path planning process of the existing UAV 3D reconstruction path planning research.Therefore,the planned path is only suitable for no-wind or light-wind scenarios.For the 3D reconstruction of ship targets,we propose a UAV path planning method that can satisfy both reconstruction efficiency and wind disturbance resistance requirements.Firstly,the concept of model surface complexity is proposed to generate a more efficient candidate view set.Secondly,the Min–Max strategy and a new viewpoint construction method are used to generate the initial path.Thirdly,combined with the wind field model,a method for generating a stable path against wind disturbance based on the idea of interval optimization is proposed.Experimental results demonstrate that our method can adaptively determine the number of sample points and viewpoints according to ship’s geometric characteristics and further reduce the number of viewpoints without significantly affecting the reconstruction quality;the path planned by our method is also stable against wind disturbance. | Tao Liu Ruiqi Shen Zhengling Lei Yuchi Huo Jiansen Zhao Xiaogang Xu | 2022 | International Journal of Digital Earth2022，15，1： | 0 |
| 3 | State of the Art on Deep Learning-enhanced Rendering Methods显示文摘Photorealistic rendering of the virtual world is an important and classic problem in the field of computer graphics.With the development of GPU hardware and continuous research on computer graphics,representing and rendering virtual scenes has become easier and more efficient.However,there are still unresolved challenges in efficiently rendering global illumination effects.At the same time,machine learning and computer vision provide real-world image analysis and synthesis methods,which can be exploited by computer graphics rendering pipelines.Deep learning-enhanced rendering combines techniques from deep learning and computer vision into the traditional graphics rendering pipeline to enhance existing rasterization or Monte Carlo integration renderers.This state-of-the-art report summarizes recent studies of deep learning-enhanced rendering in the computer graphics community.Specifically,we focus on works of renderers represented using neural networks,whether the scene is represented by neural networks or traditional scene files.These works are either for general scenes or specific scenes,which are differentiated by the need to retrain the network for new scenes. | Qi Wang Zhihua Zhong Yuchi Huo Hujun Bao Rui Wang | 2023 | Machine Intelligence Research2023，20，6： | 0 |
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