The existing panoramic image matching methods are difficult to overcome the non-uniform features of the projection transformation of the target object,and hence the issue of unstable corresponding points matching is u...The existing panoramic image matching methods are difficult to overcome the non-uniform features of the projection transformation of the target object,and hence the issue of unstable corresponding points matching is usually induced.This paper aims to solve the difficulty by proposing a sparse depth point set matching method based on panoramic disparity.By constructing a panoramic disparity model of stereo panoramic images,the disparity between corresponding points can be precisely estimated,and the robustness and effectiveness of corresponding points matching between stereo panoramic images is improved under the epipolar geometric constraints.Firstly,by defining the panoramic disparity,the corresponding angle of panoramic disparity is derived,and the matching areas of corresponding points based on the disparity corresponding angle difference are partitioned.Secondly,the optimization strategy in the matching process of corresponding points is constructed to provide stable matching results for generating sparse depth maps based on the disparity region range and epipolar geometric relationship.Experiments show that the proposed method can not only obtain more stable matching results but also exhibit higher computational efficiency than existing algorithms.展开更多
Tomographic synthetic aperture radar(TomoSAR)has the ability to separate mixed scatterers,making it highly suitable for urban 3-dimensional(3D)reconstruction.However,Urban TomoSAR imaging still faces challenges such a...Tomographic synthetic aperture radar(TomoSAR)has the ability to separate mixed scatterers,making it highly suitable for urban 3-dimensional(3D)reconstruction.However,Urban TomoSAR imaging still faces challenges such as resolution limitations,multipath effects,the uncertainty on the flight track,and registration errors,resulting in sparse point clouds with holes and low accuracy.In this paper,we propose a Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm(Geo-SETRA)for urban area.Geo-SETRA integrates geometric structures,extracted from TomoSAR point clouds,as prior distributions for elevation estimation using Bayesian methods.We first construct a sparse optimization model based on both compressed sensing and maximum a posteriori estimation,and also give its solution.Further,the Cramér-Rao lower bound of this algorithm is derived to theoretically illustrate how it improves imaging accuracy.Both simulated data and real-data experiments prove that our method is feasible and effective in urban 3D reconstruction.As a result,our method successfully produced a dense and realistic 3D scattering model for urban areas with minimal postprocessing,preserving detailed geometric structures and retaining over 80%of the points in the final model.展开更多
基金National Natural Science Foundation of China(No.41761079)Top Young Talent Project of Yunnan Province in China.
文摘The existing panoramic image matching methods are difficult to overcome the non-uniform features of the projection transformation of the target object,and hence the issue of unstable corresponding points matching is usually induced.This paper aims to solve the difficulty by proposing a sparse depth point set matching method based on panoramic disparity.By constructing a panoramic disparity model of stereo panoramic images,the disparity between corresponding points can be precisely estimated,and the robustness and effectiveness of corresponding points matching between stereo panoramic images is improved under the epipolar geometric constraints.Firstly,by defining the panoramic disparity,the corresponding angle of panoramic disparity is derived,and the matching areas of corresponding points based on the disparity corresponding angle difference are partitioned.Secondly,the optimization strategy in the matching process of corresponding points is constructed to provide stable matching results for generating sparse depth maps based on the disparity region range and epipolar geometric relationship.Experiments show that the proposed method can not only obtain more stable matching results but also exhibit higher computational efficiency than existing algorithms.
基金supported by the National Natural Science Foundation of China(grant numbers 61991421,61991424,and 61991420).
文摘Tomographic synthetic aperture radar(TomoSAR)has the ability to separate mixed scatterers,making it highly suitable for urban 3-dimensional(3D)reconstruction.However,Urban TomoSAR imaging still faces challenges such as resolution limitations,multipath effects,the uncertainty on the flight track,and registration errors,resulting in sparse point clouds with holes and low accuracy.In this paper,we propose a Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm(Geo-SETRA)for urban area.Geo-SETRA integrates geometric structures,extracted from TomoSAR point clouds,as prior distributions for elevation estimation using Bayesian methods.We first construct a sparse optimization model based on both compressed sensing and maximum a posteriori estimation,and also give its solution.Further,the Cramér-Rao lower bound of this algorithm is derived to theoretically illustrate how it improves imaging accuracy.Both simulated data and real-data experiments prove that our method is feasible and effective in urban 3D reconstruction.As a result,our method successfully produced a dense and realistic 3D scattering model for urban areas with minimal postprocessing,preserving detailed geometric structures and retaining over 80%of the points in the final model.
