The technology in modern society is very useful.China has a lot of new advanced technology.Let me introduce some to you.The first one is Artificial Intelligence(AI).China has many AI companies,such as Baidu,Alibaba an...The technology in modern society is very useful.China has a lot of new advanced technology.Let me introduce some to you.The first one is Artificial Intelligence(AI).China has many AI companies,such as Baidu,Alibaba and Tencent.They have made many things such as machine learning,natural language processing and computer vision.展开更多
Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-bas...Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-based model according to the characteristics of the surfaceto be reconstructed in RE. At last, the intensity diagrams of reflected field distribution areprovided to prove the feasibility of the presented model and some spheres are rendered with thismodel.展开更多
Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recen...Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recent years,the growing data and computing resources have promoted the development of deep learning,and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods.However,high-speed railroads,as an important part of life,have their unique industry characteristics of image data,which leads to different image fusion techniques with different fusion effects in high-speed railway scenes.This research work first introduces the mainstream technology classification of image fusion,further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes,and introduces the evaluation metrics of image fusion,followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes,and finally provides some possible future image fusion in the field of rail transportation of research.展开更多
We introduce a novel end-to-end deeplearning solution for rapidly estimating a dense spherical depth map of an indoor environment.Our input is a single equirectangular image registered with a sparse depth map,as provi...We introduce a novel end-to-end deeplearning solution for rapidly estimating a dense spherical depth map of an indoor environment.Our input is a single equirectangular image registered with a sparse depth map,as provided by a variety of common capture setups.Depth is inferred by an efficient and lightweight single-branch network,which employs a dynamic gating system to process together dense visual data and sparse geometric data.We exploit the characteristics of typical man-made environments to efficiently compress multiresolution features and find short-and long-range relations among scene parts.Furthermore,we introduce a new augmentation strategy to make the model robust to different types of sparsity,including those generated by various structured light sensors and LiDAR setups.The experimental results demonstrate that our method provides interactive performance and outperforms stateof-the-art solutions in computational efficiency,adaptivity to variable depth sparsity patterns,and prediction accuracy for challenging indoor data,even when trained solely on synthetic data without any fine tuning.展开更多
文摘The technology in modern society is very useful.China has a lot of new advanced technology.Let me introduce some to you.The first one is Artificial Intelligence(AI).China has many AI companies,such as Baidu,Alibaba and Tencent.They have made many things such as machine learning,natural language processing and computer vision.
文摘Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-based model according to the characteristics of the surfaceto be reconstructed in RE. At last, the intensity diagrams of reflected field distribution areprovided to prove the feasibility of the presented model and some spheres are rendered with thismodel.
基金supported in part by the National Key Research and Development Program of China,under Grant 2020YFB2103800.
文摘Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recent years,the growing data and computing resources have promoted the development of deep learning,and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods.However,high-speed railroads,as an important part of life,have their unique industry characteristics of image data,which leads to different image fusion techniques with different fusion effects in high-speed railway scenes.This research work first introduces the mainstream technology classification of image fusion,further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes,and introduces the evaluation metrics of image fusion,followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes,and finally provides some possible future image fusion in the field of rail transportation of research.
基金funding from the Autonomous Region of Sardinia under project XDATA.Eva Almansa,Armando Sanchez,Giorgio Vassena,and Enrico Gobbetti received funding from the European Union's H2020 research and innovation programme under grant 813170(EVOCATION).
文摘We introduce a novel end-to-end deeplearning solution for rapidly estimating a dense spherical depth map of an indoor environment.Our input is a single equirectangular image registered with a sparse depth map,as provided by a variety of common capture setups.Depth is inferred by an efficient and lightweight single-branch network,which employs a dynamic gating system to process together dense visual data and sparse geometric data.We exploit the characteristics of typical man-made environments to efficiently compress multiresolution features and find short-and long-range relations among scene parts.Furthermore,we introduce a new augmentation strategy to make the model robust to different types of sparsity,including those generated by various structured light sensors and LiDAR setups.The experimental results demonstrate that our method provides interactive performance and outperforms stateof-the-art solutions in computational efficiency,adaptivity to variable depth sparsity patterns,and prediction accuracy for challenging indoor data,even when trained solely on synthetic data without any fine tuning.
