一种非定标图像高精度三维重建算法

由非定标图像重建三维场景有着广泛的应用。给出了一种非定标多视图像三维重建算法。该算法主要基于因子分解和光束法平差技术。首先用因子分解方法得到射影空间下相机投影矩阵和物点坐标,以旋转矩阵的正交性以及对偶绝对二次曲面秩为3为约束,将射影空间升级到欧式空间,最后用光束法平差进行优化。该方法可同时获得相机的内外参数、畸变系数和场景的三维坐标。仿真实验表明,在1000 mm×1000 mm×400mm的范围内,当像点检测误差在0-1pixel和0-2pixel内,所重建三维点的误差分别为0.1530 mm和0.6712 mm。在500 mm×500 m×200 mm下,真实实验重构三维点的误差在0.3 mm以内。所提出的算法稳定可靠,可对实际工程进行指导。     

3D face reconstruction using images from cameras with varying parameters

In this paper, we present a new technique of 3D face reconstruction from a sequence of images taken with cameras having varying parameters without the need to grid. This method is based on the estimation of the projection matrices of the cameras from a symmetry property which characterizes the face, these projections matrices are used with points matching in each pair of images to determine the 3D points cloud, subsequently, 3D mesh of the face is constructed with 3D Crust algorithm. Lastly, the 2D image is projected on the 3D model to generate the texture mapping. The strong point of the proposed approach is to minimize the constraints of the calibration system: we calibrated the cameras from a symmetry property which characterizes the face, this property gives us the opportunity to know some points of 3D face in a specific well-chosen global reference, to formulate a system of linear and nonlinear equations according to these 3D points, their projection in the image plan and the elements of the projections matrix. Then to solve these equations, we use a genetic algorithm which consists of finding the global optimum without the need of the initial estimation and allows to avoid the local minima of the formulated cost function. Our study is conducted on real data to demonstrate the validity and the performance of the proposed approach in terms of robustness, simplicity, stability and convergence.     

Two-way ambiguity in 2D projective reconstruction from threeuncalibrated 1D images

We show that there is, in general, a two-way ambiguity for 2D projective reconstruction from three uncalibrated 1D views, independent of the number of point correspondences. The two distinct projective reconstructions are exactly related by a quadratic transformation with the three camera centers as fundamental points. Unique 2D reconstruction is possible only when the three camera centers are aligned. By Carlsson duality (1995), there is a dual two-way ambiguity for 2D projective reconstruction from six point correspondences, independent of the number of 1D views. The theoretical results are demonstrated on numerical examples     

A hierarchical dense deformable model for 3D face reconstruction from skull

3D face reconstruction from skull has been investigated deeply by computer scientists in the past two decades because it is important for identification. The dominant methods construct 3D face from the soft tissue thickness measured at a set of landmarks on skull. The quantity and position of the landmarks are very vital for 3D face reconstruction, but there is no uniform standard for the selection of the landmarks. Additionally, the acquirement of the landmarks on skull is difficult without manual assistance. In this paper, an automatic 3D face reconstruction method based on a hierarchical dense deformable model is proposed. To construct the model, the skull and face samples are acquired by CT scanner and represented as dense triangle mesh. Then a non-rigid dense mesh registration algorithm is presented to align all the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose and mouth. For a given skull, the globe and local deformable models are iteratively matched with it, and the reconstructed facial surface is obtained by fusing the globe and local reconstruction results. To validate the presented method, a measurement in the coefficient domain of a face deformable model is defined. The experimental results indicate that the proposed method has good performance for 3D face reconstruction from skull.     

Interactive 3D reconstruction from multiple images: A primitive-based approach

This paper proposes a fast and stable image-based modeling method which generates 3D models with high-quality face textures in a semi-automatic way. The modeler guides untrained users to quickly obtain 3D model data via several steps of simple user interface operations using predefined 3D primitives. The proposed method contains an iterative non-linear error minimization technique in the model estimation step with an error function based on finite line segments instead of infinite lines. The error corresponds to the difference between the observed structure and the predicted structure from current model parameters. Experimental results on real images validate the robustness and the accuracy of the algorithm.     

汽车类外观专利图像三维重建

为实现由汽车类外观设计专利图像到三维模型的恢复,结合汽车类外观专利图像的特点,提出融合从明暗恢复形状(SFS)与轮廓线法的汽车类外观专利图像三维重建方法.结合右视图及主视图的轮廓对由SFS得到的模型进行深度校正,得到较为精确的单幅图像三维模型;利用轮廓法恢复物体的整体模型,通过计算各单幅图像模型及整体模型的位置坐标,进行各模型之间的对齐操作,使两种方法得到的模型融为一体,得到融合模型.实验结果表明,与其它方法相比,该方法得到的三维模型在整体与细节方面都得到较大提升.     

由单幅二维灰度图像重构物体表面形状*

鉴于目前很少有论文讨论完整的由单幅二维灰度图像重构物体表面形状的算法,包括它的控制参数的估计及算法的实现,介绍了一种完整的SFS算法.它在考虑自遮掩影响的情况下,有效地估计了SFS算法中涉及的各种控制参数,并引入亮度约束、灰度梯度约束和可积性约束,计算出表面高度和表面向量,实现三维重构.与传统的算法相比,本算法无论是在速度还是在精度方面都达到了比较高的水平,具有一定的应用前景.最后还指出了在MATLAB中实现需要注意的问题.     

基于多增益参数的Ambisonic三维声像重建方法

针对固定扬声器增益参数不能同时最优地实现全部方向声像重建的问题,提出一种基于多增益的Ambisonic三维（Three-dimensional,3D）声像重建方法。该方法将空间连续的声源方位进行空间格点量化（Spatial Localization Quantization Point,SLQP）,根据量化方位所匹配的增益参数进行声像的重建。将所提方法应用于Auro-10扬声器配置中,客观测试结果表明,该方法重建声像定位质量优于传统固定增益参数方法。     

基于正交立体图像的3D人体姿态重建算法

提出一种由目标的立体图像通过人工神经网络实时估计得到其3D姿态的方法。网络的输入向量由同步立体图像帧上目标特征点的坐标构成;而输出向量则表示目标若干关键位置的三维姿态（进而可以建立目标的3D模型）。拟合该神经网络所需要的输出样本数据由运动捕获系统REACTOR获取。实验表明基于该算法的3D姿态估计误差低于5%,可以有效应用于3D虚拟目标的计算机实时合成等。     

Robust refinement methods for camera calibration and 3D reconstruction from multiple images

This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13.     

带有未知匹配的图像线特征三维重建方法的研究*

为了解决传统三维重建过程中线特征匹配速度慢、建模与匹配过程脱节的问题,提出了基于相关系数的图像线特征匹配的方法,运用此方法对同一物体的三幅图像进行了特征匹配和三维重建。建立了目标物体的三维模型,达到了对物体线特征匹配与三维重建同步进行的目的。通过对所得实验数据分析,可以证明此算法对于物体的三维重建是有效的。     

Reconstruction of 3D medical images: A nonlinear interpolation technique for reconstruction of 3D medical images

Three-dimensional medical images reconstructed from a series of two-dimensional images produced by computerized tomography, magnetic resonance imaging, etc., present a valuable tool for modern medicine. Usually, the interresolution between two cross sections is less than the intraresolution within each cross section. Therefore, interpolations are required to create a 3D visualization. Many techniques, including voxel-based and patch tiling methods, apply linear interpolations between two cross sections. Although those techniques using linear interpolations are economical in computation, they need much cross-sectional data and are unable to enlarge because of aliasing. Hence, the techniques that apply two-dimensional nonlinear interpolation functions among cross sections were proposed. In this paper, we introduce the curvature sampling of the contour of a medical object in a CT (computerized tomography) image. Those sampled contour points are the candidates for the control points of Hermite surfaces between each pair of cross sections. Then, a nearest-neighbor mapping of control points between every two cross sections is used for surface formation. The time complexity of our mapping algorithm is O(m + n), where m and n are the numbers of control points of two cross sections. It is much faster than Kehtarnavaz and De Figueiredo's merge method, whose time complexity is O(n³m²).     

3D periodic human motion reconstruction from 2D motion sequences

We present and evaluate a method of reconstructing three-dimensional (3D) periodic human motion from two-dimensional (2D) motion sequences. Using Fourier decomposition, we construct a compact representation for periodic human motion. A low-dimensional linear motion model is learned from a training set of 3D Fourier representations by means of principal components analysis. Two-dimensional test data are projected onto this model with two approaches: least-square minimization and calculation of a maximum a posteriori probability using the Bayes' rule. We present two different experiments in which both approaches are applied to 2D data obtained from 3D walking sequences projected onto a plane. In the first experiment, we assume the viewpoint is known. In the second experiment, the horizontal viewpoint is unknown and is recovered from the 2D motion data. The results demonstrate that by using the linear model, not only can missing motion data be reconstructed, but unknown view angles for 2D test data can also be retrieved.     

A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images

ABSTRACT

Lung cancer is one of the deadliest cancers in both men and women. Nowadays, several methods are used to cure this cancer including surgery and radiotherapy. These methods require prior knowledge about the shape of tumours. This type of knowledge may also help physicians to determine the cancer type. In this paper we propose a novel approach for 3D reconstruction of tumour geometry from a sequence of 2D images. The proposed approach consists of two phases: tumour segmentation from computed tomography (CT) images and 3D shape reconstruction. Segmentation is conducted using snake optimisation and Gustafson–Kessel clustering. For 3D reconstruction, first, we propose a new approach to interpolate some intermediate slices between original slices. Then, the well-known marching cubes algorithm is used for surface reconstruction. Eventually, we smoothen the surface using an explicit fairing algorithm. Experiments show that our new approach can highly improve the quality and the accuracy of the reconstructed tumour shape.     

HDR-Net-Fusion:Real-time 3D dynamic scene reconstruction with a hierarchical deep reinforcement network

Reconstructing dynamic scenes with commodity depth cameras has many applications in computer graphics,computer vision,and robotics.However,due to the presence o...     

脑部MRI图像头皮三维提取及重建

开颅手术中,为了满足手术导航系统进行快速、准确定位的要求,提出一种脑部核磁共振成像(MRI)图像三维头皮轮廓提取方法。首先采用各向异性扩散滤波方法对图像滤波,借助BrainSuite3医学软件获取脑标记图像;然后将图像中脑实质部分剔除,根据非脑组织图像计算头皮组织的分割阈值;再利用数学形态学处理二值图像获取头皮轮廓;最后结合目标灰度信息,用移动立方体(MC)算法进行三维重建。实验结果表明,该方法结合阈值、数学形态学和MC算法,能连续、光滑地提取出头皮外轮廓,并使精度达到亚像素级别。     

3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images

In medical diagnostic imaging, the X-ray CT scanner and the MRI system have been widely used to examine 3D shapes and internal structures of living organisms and bones. However, these apparatuses are generally large and very expensive. Since an appointment is also required before examination, these systems are not suitable for urgent fracture diagnosis in emergency treatment. However, X-ray/fluoroscopy has been widely used as traditional medical diagnosis. Therefore, the realization of the reconstruction of precise 3D shapes of living organisms or bones from a few conventional 2D fluoroscopic images might be very useful in practice, in terms of cost, labor, and radiation exposure. The present paper proposes a method by which to estimate a patient-specific 3D shape of a femur from only two fluoroscopic images using a parametric femoral model. First, we develop a parametric femoral model by the statistical analysis of 3D femoral shapes created from CT images of 56 patients. Then, the position and shape parameters of the parametric model are estimated from two 2D fluoroscopic images using a distance map constructed by the Level Set Method. Experiments using synthesized images, fluoroscopic images of a phantom femur, and in vivo images for hip prosthesis patients are successfully carried out, and it is verified that the proposed system has practical applications.     

单幅图像三维表面重建的算法研究与实现

以单幅二维图像为研究对象,应用SFS的基本理论,首先对二维图像进行了光照的倾角和偏角的估计,在光源坐标系下计算出物体每一点的表面梯度;之后,旋转坐标系,在观察坐标系下得到物体的表面梯度;最后采用三点辛普森公式求出物体表面的高度。整个算法流程简单,求解层次分明,重构满意度较高。     

Space and time bounds on indexing 3D models from 2D images

Model-based visual recognition systems often match groups of image features to groups of model features to form initial hypotheses, which are then verified. In order to accelerate recognition considerably, the model groups can be arranged in an index space (hashed) offline such that feasible matches are found by indexing into this space. For the case of 2D images and 3D models consisting of point features, bounds on the space required for indexing and on the speedup that such indexing can achieve are demonstrated. It is proved that, even in the absence of image error, each model must be represented by a 2D surface in the index space. This places an unexpected lower bound on the space required to implement indexing and proves that no quantity is invariant for all projections of a model into the image. Theoretical bounds on the speedup achieved by indexing in the presence of image error are also determined, and an implementation of indexing for measuring this speedup empirically is presented. It is found that indexing can produce only a minimal speedup on its own. However, when accompanied by a grouping operation, indexing can provide significant speedups that grow exponentially with the number of features in the groups