使用形状变化描述三维模型

针对三维模型检索中的形状特征提取问题,提出利用三维模型自身形状变化信息构造形状特征描述符的方法.首先选择一组等间距互相平行的平面切割三维模型,得到三维模型的切片集合;然后定义相邻切片的 差来描述切片间的形状变化,并通过所有相邻切片间的差值来反映三维模型自身的形状变化,以此作为三维模型的形状特征描述符.该方法与三维模型的旋转、平移无关,同时不依赖于模型的点云分布,并且精简模型三角面片对算法的影响较小.实验结果验证了该方法的有效性.     

Comparative analysis of shape descriptors for 3D objects

Multimedia Tools and Applications - One of the basic characteristics of an object is its shape. Several research areas in mathematics and computer science have taken an interest in object...     

Representation of 3D surfaces by two-variable Fourier descriptors

A method for generating two-variable 3D FDs directly from a striped lighting system is developed. An iterative algorithm is proposed to compute the two-variable 3D FDs for both axisymmetric and nonaxisymmetric objects and a formula for convergence test is derived. Experiments conducted for a set of 3D objects show that the iterative algorithm converges very quickly and the two-variable 3D FD representations are attained accurately     

不规则形状图像曲率的自适应计算方法

提出一种针对不规则多形变红血球图像的自适应曲面拟合并计算曲率的方法,通过阴影恢复技术重构细胞表面的高度场形状,利用三维数据点根据最小二乘法进行曲面拟合,选定深度均方误差阈值来决定参与拟合的邻域点.计算得到的高斯曲率、平均曲率可用采表征某一点的表面类型,主曲率则用来观察曲面变化较大的区域.实验结果表明,该方法具有很强的可行性与实用性.     

Restoring warped document images through 3D shape modeling

Scanning a document page from a thick bound volume often results in two kinds of distortions in the scanned image, i.e., shade along the "spine" of the book and warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. From a technical point of view, this shape from shading (SFS) problem in real-world environments is characterized by 1) a proximal and moving light source, 2) Lambertian reflection, 3) nonuniform albedo distribution, and 4) document skew. Taking all these factors into account, we first build practical models (consisting of a 3D geometric model and a 3D optical model) for the practical scanning conditions to reconstruct the 3D shape of the book surface. We next restore the scanned document image using this shape based on deshading and dewarping models. Finally, we evaluate the restoration results by comparing our estimated surface shape with the real shape as well as the OCR performance on original and restored document images. The results show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.     

Conveying the 3D shape of smoothly curving transparent surfaces viatexture

Transparency can be a useful device for depicting multiple overlapping surfaces in a single image. The challenge is to render the transparent surfaces in such a way that their 3D shape can be readily understood and their depth distance from underlying structures clearly perceived. This paper describes our investigations into the use of sparsely-distributed discrete, opaque texture as an artistic device for more explicitly indicating the relative depth of a transparent surface and for communicating the essential features of its 3D shape in an intuitively meaningful and minimally occluding way. The driving application for this work is the visualization of layered surfaces in radiation therapy treatment planning data, and the technique is illustrated on transparent isointensity surfaces of radiation dose. We describe the perceptual motivation and artistic inspiration for defining a stroke texture that is locally oriented in the direction of greatest normal curvature (and in which individual strokes are of a length proportional to the magnitude of the curvature in the direction they indicate), and we discuss two alternative methods for applying this texture to isointensity surfaces defined in a volume. We propose an experimental paradigm for objectively measuring observers' ability to judge the shape and depth of a layered transparent surface, in the course of a task which is relevant to the needs of radiotherapy treatment planning, and use this paradigm to evaluate the practical effectiveness of our approach through a controlled observer experiment based on images generated from actual clinical data     

Sampling framework for accurate curvature estimation in discrete surfaces

Accurate curvature estimation in discrete surfaces is an important problem with numerous applications. Curvature is an indicator of ridges and can be used in applications such as shape analysis and recognition, object segmentation, adaptive smoothing, anisotropic fairing of irregular meshes, and anisotropic texture mapping. In this paper, a new framework is proposed for accurate curvature estimation in discrete surfaces. The proposed framework is based on a local directional curve sampling of the surface where the sampling frequency can be controlled. This local model has a large number of degrees of freedoms compared with known techniques and, so, can better represent the local geometry. The proposed framework is quantitatively evaluated and compared with common techniques for surface curvature estimation. In order to perform an unbiased evaluation in which smoothing effects are factored out, we use a set of randomly generated Bezier surface patches for which the curvature values can be analytically computed. It is demonstrated that, through the establishment of sampling conditions, the error in estimations obtained by the proposed framework is smaller and that the proposed framework is less sensitive to low sampling density, sampling irregularities, and sampling noise.     

Multiple human 3D pose estimation from multiview images

Multimedia Tools and Applications - Multiple human 3D pose estimation is a challenging task. It is mainly because of large variations in the scale and pose of humans, fast motions, multiple persons...     

Correction for the dislocation of curved surfaces caused by the PSF in 2D and 3D CT images

Conventional edge-detection methods suffer from the dislocation of curved surfaces due to the PSF. We propose a new method that uses the isophote curvature to circumvent this. It is accurate for objects with locally constant curvature, even for small objects (like blood vessels) and in the presence of noise.     

Accurate and fast 3D head pose estimation with noisy RGBD images

Head pose estimation plays an essential role in many high-level face analysis tasks. However, accurate and robust pose estimation with existing approaches remains challenging. In this paper, we propose a novel method for accurate three-dimensional (3D) head pose estimation with noisy depth maps and high-resolution color images that are typically produced by popular RGBD cameras such as the Microsoft Kinect. Our method combines the advantages of the high-resolution RGB image with the 3D information of the depth image. For better accuracy and robustness, features are first detected using only the color image, and then the 3D feature points used for matching are obtained by combining depth information. The outliers are then filtered with depth information using rules proposed for depth consistency, normal consistency, and re-projection consistency, which effectively eliminate the influence of depth noise. The pose parameters are then iteratively optimized using the Extended LM (Levenberg-Marquardt) method. Finally, a Kalman filter is used to smooth the parameters. To evaluate our method, we built a database of more than 10K RGBD images with ground-truth poses recorded using motion capture. Both qualitative and quantitative evaluations show that our method produces notably smaller errors than previous methods.     

Fast joint estimation of silhouettes and dense 3D geometry from multiple images

We propose a probabilistic formulation of joint silhouette extraction and 3D reconstruction given a series of calibrated 2D images. Instead of segmenting each image separately in order to construct a 3D surface consistent with the estimated silhouettes, we compute the most probable 3D shape that gives rise to the observed color information. The probabilistic framework, based on Bayesian inference, enables robust 3D reconstruction by optimally taking into account the contribution of all views. We solve the arising maximum a posteriori shape inference in a globally optimal manner by convex relaxation techniques in a spatially continuous representation. For an interactively provided user input in the form of scribbles specifying foreground and background regions, we build corresponding color distributions as multivariate Gaussians and find a volume occupancy that best fits to this data in a variational sense. Compared to classical methods for silhouette-based multiview reconstruction, the proposed approach does not depend on initialization and enjoys significant resilience to violations of the model assumptions due to background clutter, specular reflections, and camera sensor perturbations. In experiments on several real-world data sets, we show that exploiting a silhouette coherency criterion in a multiview setting allows for dramatic improvements of silhouette quality over independent 2D segmentations without any significant increase of computational efforts. This results in more accurate visual hull estimation, needed by a multitude of image-based modeling approaches. We made use of recent advances in parallel computing with a GPU implementation of the proposed method generating reconstructions on volume grids of more than 20 million voxels in up to 4.41 seconds.     

A new shape preserving parallel thinning algorithm for 3D digital images

This paper is concerned with a new parallel thinning algorithm for three-dimensional digital images that preserves the topology and maintains their shape. We introduce an approach of selecting shape points and outer-layer used for erosion during each iteration. The approach produces good skeleton for different types of corners. The concept of using two image versions in thinning is introduced and its necessity in parallel thinning is justified. The robustness of the algorithm under pseudo-random noise as well as rotation with respect to shape properties is studied and the results are found to be satisfactory.     

Towards an efficient 3D model estimation methodology for aerial and ground images

In this paper we propose an efficient approach for automatic generation of 3D models from images based on structure from motion (SfM) and multi-view stereo reconstruction techniques. Current imaging devices are capable of producing high-definition images and are an ubiquitous payload of unmanned aerial vehicles. However, the time required to obtain models quickly becomes prohibitive as the number of images increases. In our approach, which is image-based only, we use meta-data information such as GPS, keypoint filtering and multiple local bundle adjustment refinement instead of global optimization in a novel scheme to speed up the incremental SfM process. The results from real data show that our approach outperforms the time performance of current strategies while maintaining the quality of the resulting model. Experiments with an unorganized set of images were also conducted, and the results show that our method is able to efficiently estimate 3D models from collections of images with reduced re-projection error.     

Local surface shape estimation of 3-D textured surfaces usingGaussian Markov random fields and stereo windows

The problem of extracting the local shape information of a 3-D texture surface from a single 2-D image by tracking the perceived systematic deformations the texture undergoes by virtue of being present on a 3-D surface and by virtue of being imaged is examined. The surfaces of interest are planar and developable surfaces. The textured objects are viewed as originating by laying a rubber planar sheet with a homogeneous parent texture on it onto the objects. The homogeneous planar parent texture is modeled by a stationary Gaussian Markov random field (GMRF). A probability distribution function for the texture data obtained by projecting the planar parent texture under a linear camera model is derived, which is an explicit function of the parent GMRF parameters, the surface shape parameters. and the camera geometry. The surface shape parameter estimation is posed as a maximum likelihood estimation problem. A stereo-windows concept is introduced to obtain a unique and consistent parent texture from the image data that, under appropriate transformations, yields the observed texture in the image. The theory is substantiated by experiments on synthesized as well as real images of textured surfaces     

3D shape estimation in video sequences provides high precision evaluation of facial expressions

Person independent and pose invariant estimations of facial expressions and action unit (AU) intensity estimation are important for situation analysis and for automated video annotation. We evaluated raw 2D shape data of the CK+ database, used Procrustes transformation and the multi-class SVM leave-one-out method for classification. We found close to 100% performance demonstrating the relevance and the strength of details of the shape. Precise 3D shape information was computed by means of constrained local models (CLM) on video sequences. Such sequences offer the opportunity to compute a time-averaged ‘3D personal mean shape’ (PMS) from the estimated CLM shapes, which – upon subtraction – gives rise to person independent emotion estimation. On CK+ data PMS showed significant improvements over AU0 normalization; performance reached and sometimes surpassed state-of-the-art results on emotion classification and on AU intensity estimation. 3D PMS from 3D CLM offers pose invariant emotion estimation that we studied by rendering a 3D emotional database for different poses and different subjects from the BU 4DFE database. Frontal shapes derived from CLM fits of the 3D shape were evaluated. Results demonstrate that shape estimation alone can be used for robust, high quality pose invariant emotion classification and AU intensity estimation.