Omni-directional imaging system is becoming more and more common in reducing the maintenance fees and the number of cameras used as well as increasing the angle of view in a single camera. Due to omni-directional imag...Omni-directional imaging system is becoming more and more common in reducing the maintenance fees and the number of cameras used as well as increasing the angle of view in a single camera. Due to omni-directional images are not directly understandable, an approach namely the un-warping process, has been implemented in converting the omni-directional image to a panoramic image, making it understandable. There are different kinds of methods used for the implementation of this approach. This paper evaluates the performance of the 3 universal un-warping methods currently applied actively around the world in transforming omni-directional image to panoramic image, namely the pano-mapping table method, discrete geometry method （DGT） and the log-polar mapping method. The algorithm of these methods will first be proposed, and the code will then be generated and be tested on several different omni-directional images. The images converted will then be compared among each other and be evaluated based on their performance on the resolutions, quality, algorithm used, complexity based on Big-O computations, processing time, and finally their data compression rate available for each of the methods. The most preferable un-warping method will then be concluded, taking into considerations all these factors.展开更多
This paper surveys the technology used in three-dimensional indoor scene geometry estimation from a single 360°omnidirectional image,which is pivotal in extracting 3D structural information from indoor environmen...This paper surveys the technology used in three-dimensional indoor scene geometry estimation from a single 360°omnidirectional image,which is pivotal in extracting 3D structural information from indoor environments.The technology transforms omnidirectional data into a 3D model,depicting spatial structure,object positions,and scene layout.Its significance spans various domains,including virtual reality(VR),augmented reality(AR),mixed reality(MR),game development,urban planning,and robot navigation.We begin by revisiting foundational concepts of omnidirectional imaging and detailing the problems,applications,and challenges in this field.Our review categorizes the fundamental tasks of structure recovery,depth estimation,and layout recovery.We also review pertinent datasets and evaluation metrics,providing the latest research as a reference.Finally,we summarize the field and discuss potential future trends to inform and guide further research.展开更多
文摘Omni-directional imaging system is becoming more and more common in reducing the maintenance fees and the number of cameras used as well as increasing the angle of view in a single camera. Due to omni-directional images are not directly understandable, an approach namely the un-warping process, has been implemented in converting the omni-directional image to a panoramic image, making it understandable. There are different kinds of methods used for the implementation of this approach. This paper evaluates the performance of the 3 universal un-warping methods currently applied actively around the world in transforming omni-directional image to panoramic image, namely the pano-mapping table method, discrete geometry method （DGT） and the log-polar mapping method. The algorithm of these methods will first be proposed, and the code will then be generated and be tested on several different omni-directional images. The images converted will then be compared among each other and be evaluated based on their performance on the resolutions, quality, algorithm used, complexity based on Big-O computations, processing time, and finally their data compression rate available for each of the methods. The most preferable un-warping method will then be concluded, taking into considerations all these factors.
基金supported by the National Natural Science Foundation of China(11571325)the State Key Laboratory of Virtual Reality Technology and Systems,Beihang University(VRLAB2023C04)+1 种基金the Fundamental Research Funds for the Central Universities(CUC22GP005,CUC2019A002)Pubic Computing Cloud,CUC.
文摘This paper surveys the technology used in three-dimensional indoor scene geometry estimation from a single 360°omnidirectional image,which is pivotal in extracting 3D structural information from indoor environments.The technology transforms omnidirectional data into a 3D model,depicting spatial structure,object positions,and scene layout.Its significance spans various domains,including virtual reality(VR),augmented reality(AR),mixed reality(MR),game development,urban planning,and robot navigation.We begin by revisiting foundational concepts of omnidirectional imaging and detailing the problems,applications,and challenges in this field.Our review categorizes the fundamental tasks of structure recovery,depth estimation,and layout recovery.We also review pertinent datasets and evaluation metrics,providing the latest research as a reference.Finally,we summarize the field and discuss potential future trends to inform and guide further research.
