Lane detection is essential for many aspects of autonomous driving,such as lane-based navigation and high-definition(HD)map modeling.Although lane detection is challenging especially with complex road conditions,consi...Lane detection is essential for many aspects of autonomous driving,such as lane-based navigation and high-definition(HD)map modeling.Although lane detection is challenging especially with complex road conditions,considerable progress has been witnessed in this area in the past several years.In this survey,we review recent visual-based lane detection datasets and methods.For datasets,we categorize them by annotations,provide detailed descriptions for each category,and show comparisons among them.For methods,we focus on methods based on deep learning and organize them in terms of their detection targets.Moreover,we introduce a new dataset with more detailed annotations for HD map modeling,a new direction for lane detection that is applicable to autonomous driving in complex road conditions,a deep neural network LineNet for lane detection,and show its application to HD map modeling.展开更多
Indoor scene synthesis has become a popular topic in recent years.Synthesizing functional and plausible indoor scenes is an inherently difficult task since it requires considerable knowledge to both choose reasonable ...Indoor scene synthesis has become a popular topic in recent years.Synthesizing functional and plausible indoor scenes is an inherently difficult task since it requires considerable knowledge to both choose reasonable object categories and arrange objects appropriately.In this survey,we propose four criteria which group a wide range of 3D(three-dimensional)indoor scene synthesis techniques according to various aspects(specifically,four groups of categories).It also provides hints,througli comprehensively comparing all the techniques to demonstrate their effectiveness and drawbacks,and discussions of potential remaining problems.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61902210 and 61521002a research grant from the Beijing Higher Institution Engineering Research Center,and the Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.
文摘Lane detection is essential for many aspects of autonomous driving,such as lane-based navigation and high-definition(HD)map modeling.Although lane detection is challenging especially with complex road conditions,considerable progress has been witnessed in this area in the past several years.In this survey,we review recent visual-based lane detection datasets and methods.For datasets,we categorize them by annotations,provide detailed descriptions for each category,and show comparisons among them.For methods,we focus on methods based on deep learning and organize them in terms of their detection targets.Moreover,we introduce a new dataset with more detailed annotations for HD map modeling,a new direction for lane detection that is applicable to autonomous driving in complex road conditions,a deep neural network LineNet for lane detection,and show its application to HD map modeling.
基金the National Key Technology Researcli and Development Program under Grant No.2017YFB1002604the National Natural Science Foundation of China under Grant Nos.61772298 and 61832016the Research Grant of Beijing Higher Institution Engineering Research Center and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.
文摘Indoor scene synthesis has become a popular topic in recent years.Synthesizing functional and plausible indoor scenes is an inherently difficult task since it requires considerable knowledge to both choose reasonable object categories and arrange objects appropriately.In this survey,we propose four criteria which group a wide range of 3D(three-dimensional)indoor scene synthesis techniques according to various aspects(specifically,four groups of categories).It also provides hints,througli comprehensively comparing all the techniques to demonstrate their effectiveness and drawbacks,and discussions of potential remaining problems.
