The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and ...The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and transmission abilities of well logging instruments are much limited due to the underground environment,it is necessary to explore suitable sampling methods which can be used to obtain an accurate RST with less sampling data.This paper presents a uniform amplitude sampling method（UASM）,and compares it with the conventional uniform time sampling method（UTSM） and logarithm time sampling method（LTSM） in terms of the adaptability to different strata,RTS inversion accuracy,and stratum vertical resolution.Numerical simulation results show that the UASM can obtain high inversion accuracy of RTS with different kinds of pore size distribution formation,with high dynamic ranges of pore size,and with a small number of sampling points.The UASM,being able to adapt to the attenuation speed of polarization curve automatically,thus has the highest vertical resolution.The inversion results of rock samples also show that the UASM is superior to the UTSM and LTSM.展开更多
Normal copula with a correlation coefficient between-1 and 1 is tail independent and so it severely underestimates extreme probabilities. By letting the correlation coefficient in a normal copula depend on the sample ...Normal copula with a correlation coefficient between-1 and 1 is tail independent and so it severely underestimates extreme probabilities. By letting the correlation coefficient in a normal copula depend on the sample size, H¨usler and Reiss(1989) showed that the tail can become asymptotically dependent. We extend this result by deriving the limit of the normalized maximum of n independent observations, where the i-th observation follows from a normal copula with its correlation coefficient being either a parametric or a nonparametric function of i/n. Furthermore, both parametric and nonparametric inference for this unknown function are studied, which can be employed to test the condition by H¨usler and Reiss(1989). A simulation study and real data analysis are presented too.展开更多
Photometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination.Traditional methods normally adopt simplified reflectance...Photometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination.Traditional methods normally adopt simplified reflectance models to make the surface orientation computable.However,the real reflectances of surfaces greatly limit applicability of such methods to real-world objects.While deep neural networks have been employed to handle non-Lambertian surfaces,these methods are subject to blurring and errors,especially in high-frequency regions(such as crinkles and edges),caused by spectral bias:neural networks favor low-frequency representations so exhibit a bias towards smooth functions.In this paper,therefore,we propose a self-learning conditional network with multiscale features for photometric stereo,avoiding blurred reconstruction in such regions.Our explorations include:(i)a multi-scale feature fusion architecture,which keeps high-resolution representations and deep feature extraction,simultaneously,and(ii)an improved gradient-motivated conditionally parameterized convolution(GM-CondConv)in our photometric stereo network,with different combinations of convolution kernels for varying surfaces.Extensive experiments on public benchmark datasets show that our calibrated photometric stereo method outperforms the state-of-the-art.展开更多
This study focuses on multi-human relief modeling using a single photograph.Although previous studies successfully modeled 3D humans from single photographs,they were limited to reconstructing 3D individuals and could...This study focuses on multi-human relief modeling using a single photograph.Although previous studies successfully modeled 3D humans from single photographs,they were limited to reconstructing 3D individuals and could not be applied to multi-human scenes with complex inter-body and outer-body occlusions.In this study,we introduce MMRelief,a novel solution that takes a significant step toward high-quality and generalized multi-human relief modeling.MMRelief uses a three-step approach to achieve its objectives.First,it predicts an occlusion-aware depth map based on ZoeDepth[12].Subsequently,it predicts a detailed normal map using a photo-to-normal network.Finally,MMRelief combines the strengths of both maps and constructs human relief using depth-constrained normal integration.Experimental results demonstrate that MMRelief has achieved state-of-the-art performance in normal human estimation.It can handle different styles of human photos with varying poses and dresses while producing reliefs with accurate body occlusions,reasonable depth ordering,and faithful geometrical details.The project page is at https://github.com/yanqingliu3856/MMRelief.展开更多
基金partially supported by a project from the National Natural Science Foundation of China (No.61401168)
文摘The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and transmission abilities of well logging instruments are much limited due to the underground environment,it is necessary to explore suitable sampling methods which can be used to obtain an accurate RST with less sampling data.This paper presents a uniform amplitude sampling method（UASM）,and compares it with the conventional uniform time sampling method（UTSM） and logarithm time sampling method（LTSM） in terms of the adaptability to different strata,RTS inversion accuracy,and stratum vertical resolution.Numerical simulation results show that the UASM can obtain high inversion accuracy of RTS with different kinds of pore size distribution formation,with high dynamic ranges of pore size,and with a small number of sampling points.The UASM,being able to adapt to the attenuation speed of polarization curve automatically,thus has the highest vertical resolution.The inversion results of rock samples also show that the UASM is superior to the UTSM and LTSM.
基金supported by the Simons FoundationNational Natural Science Foundation of China(Grant No.11171275)the Natural Science Foundation Project of CQ(Grant No.cstc2012jj A00029)
文摘Normal copula with a correlation coefficient between-1 and 1 is tail independent and so it severely underestimates extreme probabilities. By letting the correlation coefficient in a normal copula depend on the sample size, H¨usler and Reiss(1989) showed that the tail can become asymptotically dependent. We extend this result by deriving the limit of the normalized maximum of n independent observations, where the i-th observation follows from a normal copula with its correlation coefficient being either a parametric or a nonparametric function of i/n. Furthermore, both parametric and nonparametric inference for this unknown function are studied, which can be employed to test the condition by H¨usler and Reiss(1989). A simulation study and real data analysis are presented too.
基金supported by the National Key Scientific Instrument and Equipment Development Projects of China(41927805)the National Natural Science Foundation of China(61501417,61976123)+1 种基金the Key Development Program for Basic Research of Shandong Province(ZR2020ZD44)the Taishan Young Scholars Program of Shandong Province.
文摘Photometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination.Traditional methods normally adopt simplified reflectance models to make the surface orientation computable.However,the real reflectances of surfaces greatly limit applicability of such methods to real-world objects.While deep neural networks have been employed to handle non-Lambertian surfaces,these methods are subject to blurring and errors,especially in high-frequency regions(such as crinkles and edges),caused by spectral bias:neural networks favor low-frequency representations so exhibit a bias towards smooth functions.In this paper,therefore,we propose a self-learning conditional network with multiscale features for photometric stereo,avoiding blurred reconstruction in such regions.Our explorations include:(i)a multi-scale feature fusion architecture,which keeps high-resolution representations and deep feature extraction,simultaneously,and(ii)an improved gradient-motivated conditionally parameterized convolution(GM-CondConv)in our photometric stereo network,with different combinations of convolution kernels for varying surfaces.Extensive experiments on public benchmark datasets show that our calibrated photometric stereo method outperforms the state-of-the-art.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61772293 and 62072274in part by the Joint Fund of the National Natural Science Foundation of China under Grant No.U22A2033.
文摘This study focuses on multi-human relief modeling using a single photograph.Although previous studies successfully modeled 3D humans from single photographs,they were limited to reconstructing 3D individuals and could not be applied to multi-human scenes with complex inter-body and outer-body occlusions.In this study,we introduce MMRelief,a novel solution that takes a significant step toward high-quality and generalized multi-human relief modeling.MMRelief uses a three-step approach to achieve its objectives.First,it predicts an occlusion-aware depth map based on ZoeDepth[12].Subsequently,it predicts a detailed normal map using a photo-to-normal network.Finally,MMRelief combines the strengths of both maps and constructs human relief using depth-constrained normal integration.Experimental results demonstrate that MMRelief has achieved state-of-the-art performance in normal human estimation.It can handle different styles of human photos with varying poses and dresses while producing reliefs with accurate body occlusions,reasonable depth ordering,and faithful geometrical details.The project page is at https://github.com/yanqingliu3856/MMRelief.
