Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation

We present a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking and easy-to-compute smoothing operator that fairs the surface separately along its tangential and normal directions. The mesh restructuring operator, which is based on edge split, collapse and flip operations, enables the deformable mesh to adapt its shape to the object geometry without suffering from any geometrical distortions. By imposing appropriate minimum and maximum edge length constraints, the deformable mesh, hence the object surface, can be represented at increasing levels of detail. This coarse-to-fine strategy, that allows high resolution reconstructions even with deficient and irregularly sampled range data, not only provides robustness, but also significantly improves the computational efficiency of the deformation process. We demonstrate the performance of the proposed method on several real objects.     

A frequency domain technique for range data registration

This work introduces an original method for registering pairs of 3D views consisting of range data sets which operates in the frequency domain. The Fourier transform allows the decoupling of the estimate of the rotation parameters from the estimate of the translation parameters, our algorithm exploits this well-known property by suggesting a three-step procedure. The rotation parameters are estimated by the first two steps through convenient representations and projections of the Fourier transforms' magnitudes and the translational displacement is recovered by the third step by means of a standard phase correlation technique after compensating one of the two views for rotation. The performance of the algorithm, which is well-suited for unsupervised registration, is clearly assessed through extensive testing with several objects and shows that good and robust estimates of 3D rigid motion are achievable. Our algorithm can be used as a prealignment tool for more accurate space-domain registration techniques, like the ICP algorithm.     

3D reconstruction using silhouettes from unordered viewpoints

In this paper, we present a novel approach for reconstructing an object surface from its silhouettes. The proposed approach directly estimates the differential structure of the surface, and results in a higher accuracy than existing volumetric approaches for object reconstruction. Compared with other existing differential approaches, our approach produces relatively complete 3D models similar to volumetric approaches, with the topology conforming to what is observed from the silhouettes. In addition, the method neither assumes nor depends on the spatial order of viewpoints. Experimental results on both synthetic and real world data are presented, and comparison is made with other existing approaches to demonstrate the superiority of the proposed approach.     

A maximum-likelihood surface estimator for dense range data

Describes how to estimate 3D surface models from dense sets of noisy range data taken from different points of view, i.e., multiple range maps. The proposed method uses a sensor model to develop an expression for the likelihood of a 3D surface, conditional on a set of noisy range measurements. Optimizing this likelihood with respect to the model parameters provides an unbiased and efficient estimator. The proposed numerical algorithms make this estimation computationally practical for a wide variety of circumstances. The results from this method compare favorably with state-of-the-art approaches that rely on the closest-point or perpendicular distance metric, a convenient heuristic that produces biased solutions and fails completely when surfaces are not sufficiently smooth, as in the case of complex scenes or noisy range measurements. Empirical results on both simulated and real ladar data demonstrate the effectiveness of the proposed method for several different types of problems. Furthermore, the proposed method offers a general framework that can accommodate extensions to include surface priors, more sophisticated noise models, and other sensing modalities, such as sonar or synthetic aperture radar.     

Three-dimensional human shape inference from silhouettes: reconstruction and validation

Silhouettes are robust image features that provide considerable evidence about the three-dimensional (3D) shape of a human body. The information they provide is, however, incomplete and prior knowledge has to be integrated to reconstruction algorithms in order to obtain realistic body models. This paper presents a method that integrates both geometric and statistical priors to reconstruct the shape of a subject assuming a standardized posture from a frontal and a lateral silhouette. The method is comprised of three successive steps. First, a non-linear function that connects the silhouette appearances and the body shapes is learnt and used to create a first approximation. Then, the body shape is deformed globally along the principal directions of the population (obtained by performing principal component analysis over 359 subjects) to follow the contours of the silhouettes. Finally, the body shape is deformed locally to ensure it fits the input silhouettes as well as possible. Experimental results showed a mean absolute 3D error of 8 mm with ideal silhouettes extraction. Furthermore, experiments on body measurements (circumferences or distances between two points on the body) resulted in a mean error of 11 mm.     

Computing on rays: A parallel approach for surface mesh modeling from multi-material volumetric data

Ray representation (Ray-rep) of a solid has been studied and used in the solid modeling community for many years because of its compactness and simplicity. This paper presents a parallel approach for mesh surface modeling from multi-material volume data using an extended Ray-rep as an intermediate, where every homogeneous region is enclosed by a set of two-manifold surface meshes on the resultant model. The approach consists of three major algorithms: firstly, an algorithm is developed to convert the given multi-material volumetric data into a Ray-rep for heterogeneous solid; secondly, filtering algorithm is exploited to process the rays of heterogeneous solid in parallel; and lastly, the adaptive mesh surfaces are generated from the Ray-rep through a dual-contouring like algorithm. Here the intermediate surfaces between two constituent materials can be directly extracted without building the volumetric mesh, and the manifold topology is preserved on each surface patch. Furthermore, general offset surface can be easily computed in this paradigm by designing a special parallel operator for the rays.     

Robust deformation capture from temporal range data for surface rendering

Imagine an object such as a paper sheet being waved in front of some sensor. Reconstructing the time‐varying 3D shape of the object finds direct applications in computer animation. The goal of this paper is to provide such a deformation capture system for surfaces. It uses temporal range data obtained by sensors such as those based on structured light or stereo. So as to deal with many different kinds of material, we do not make the usual assumption that the object surface has textural information. This rules out those techniques based on detecting and matching keypoints or directly minimizing color discrepancy. The proposed method is based on a planar mesh that is deformed so as to fit each of the range images. We show how to achieve this by minimizing a compound cost function combining several data and regularization terms, needed to make the overall system robust so that it can deal with low quality datasets. Carefully examining the parameter to residual relationship shows that this cost function can be minimized very efficiently by coupling nonlinear least squares methods with sparse matrix operators. Experimental results for challenging datasets coming from different kinds of range sensors are reported. The algorithm is reasonably fast and is shown to be robust to missing and erroneous data points. Copyright © 2008 John Wiley & Sons, Ltd.     

Invariant surface and motion estimation from sparse range data

In this paper, we present a system for the estimation of the surface structure and the motion parameters of a free-flying object in a tele-robotics experiment. The system consists of two main components: (i) a vision-based invariant-surface and motion estimator and (ii) a Kalman filter state estimator. We present a new algorithm for motion estimation from sparse multi-sensor range data. The motion estimates from the vision-based estimator are input to a Kalman filter state estimator for continuously tracking a free-flying object in space under zero-gravity conditions. The predicted position and orientation parameters are then fed back to the vision module of the system and serve as an initial guess in the search for optimal motion parameters. The task of the vision module is two-fold: (i) estimating a piecewise-smooth surface from a single frame of multi-sensor data and (ii) determining the most likely (in the Bayesian sense) object motion that makes data in subsequent time frames to have been sampled from the same piecewise-smooth surface. With each incoming data frame, the piecewise-smooth surface is incrementally refined. The problem is formulated as an energy minimization and solved numerically resulting in a surface estimate invariant to 3D rigid motion and the vector of motion parameters. Performance of the system is depicted on simulated and real range data.     

A type-merging algorithm for extracting an isosurface from volumetric data

A new approach for reducing the number of triangles representing an isosurface in volumetric data is presented. The basic idea is to classify the configurations of the marching cubes approach into types. Surface patches traversing neighboring cubes of the same type can be merged into patches, which can be approximated with fewer and larger triangles. Experimental results show that the number of triangles is about 50% of that obtained with the marching cubes algorithm, with comparable image quality. The execution time is somewhat longer than that of the marching cubes algorithm.     

A fast display method for volumetric data

Presented is a fast display method for volumetric data sets, which involves a slicebased method for extracting potentially visible voxels to represent visible surfaces. For a given viewing direction, the number of visible voxels can be trimmed further by culling most of the voxels not visible from that direction. The entire 3D array of voxels is also present for invasive operations and direct access to interior structures. This approach has been integrated on a low-cost graphic engine as an interactive system for craniofacial surgical planning that is currently in clinical use.     

Identifying rectangles in laser range data for urban scene reconstruction

In urban scenes, many of the surfaces are planar and bounded by simple shapes. In a laser scan of such a scene, these simple shapes can still be identified. We present a one-parameter algorithm that can identify point sets on a plane for which a rectangle is a fitting boundary. These rectangles have a guaranteed density: no large part of the rectangle is empty of points. We prove that our algorithm identifies all angles for which a rectangle fits the point set of size n in O(nlogn) time. We evaluate our method experimentally on 13 urban data sets and we compare the rectangles found by our algorithm to the α‐shape as a surface boundary.     

Automatic urban modeling using volumetric reconstruction with surface graph cuts

The demand for 3D city-scale models has been significantly increased due to the proliferation of urban planning, city navigation, and virtual reality applications. We present an approach to automatically reconstruct buildings densely spanning a large urban area. Our method takes as input calibrated aerial images and available GIS meta-data. Our computational pipeline computes a per-building 2.5D volumetric reconstruction by exploiting photo-consistency where it is highly sampled amongst the aerial images. Our building surface graph cut method overcomes errors of occlusion, geometry, and calibration in order to stitch together aerial images and yield a visually coherent texture-mapped result. Our comparisons show similar quality to the manually modeled buildings of Google Earth, and show improvements over naive texture mapping and over space-carving methods. We have tested our algorithms with a 12 sq km area of Boston, MA (USA), using 4667 images (i.e., 280 GB of raw image data) and producing 1785 buildings.     

多传感器数据融合技术及其应用

多传感器数据融合技术是一门新兴前沿技术。近年来,多传感器数据融合技术已受到广泛关注,它的理论和方法已被应用到许多研究领域。主要论述了多传感器数据融合的基本概念、工作原理、数据融合特点与结构、数据融合方法及其应用领域,并总结了当前数据融合研究中存在的主要问题及其发展趋势。     

A new implicit blending technique for volumetric modelling

Current implicit blending techniques are mostly designed for use in surface modelling, where only boundaries of the object defined by the implicit primitives are important. In contrast, in volumetric implicit modelling the interior of the object is also significant, which requires different and more suitable techniques for combining implicit primitives. In this paper, we first discuss irregularities that occur using the current techniques. Then, a new technique for blending implicit primitives, especially appropriate in volumetric modelling (e.g., cloud modelling), is introduced. It overcomes these abnormalities and gives us better results than current techniques.     

基于RBF神经网络的点云数据曲面重建快速算法

在分析现有重构方法局限性的基础上,给出了一种基于神经网络的点云数据重构三维网格形状的快速算法。首先对点云数据进行归一化处理;然后进行特征线提取,并以特征线为基础对曲面进行分割。该方法能直接从神经网络的权值矩阵得到曲线的控制顶点或曲面的控制网格,通过神经网络的权值约束实现曲线段或曲面片之间的连接。实验结果表明,使用该方法能快速获得形状良好的网格曲面。     

A general framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features

In this paper a novel framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features is presented. Based on the analysis of at least two pixel-synchronous images of the scene under different illumination conditions, this framework combines a shape from shading approach for estimating surface gradients and altitude variations on small scales with a shadow analysis method that allows for the determination of the large-scale properties of the surface. As a first step, the result of shadow analysis is used for selecting a consistent solution of the shape from shading reconstruction algorithm. As a second step, an additional error term derived from the fine-structure of the shadow is incorporated into the reconstruction algorithm. This approach is extended to the analysis of two or more shadows under different illumination conditions leading to an appropriate initialization of the shape from shading algorithm. The framework is applied to the astrogeological task of three-dimensional reconstruction of regions on the lunar surface using ground-based CCD images and to the machine vision task of industrial quality inspection.     

汽车仪表系统中的数据融合技术研究

探讨汽车仪表系统中适用的数据融合技术。运用算术平均值算法与分批估计相结合的融合算法,提高单传感器数据的测量精度;提出的汽车仪表系统中基于最大隶属度的多传感器数据融合方法,则提供了安全决策辅助,增加了仪表系统的新功能。测量实例证明了这些方法在汽车仪表系统中应用的有效性和准确性。     

拉伸与旋转面轮廓数据分段及约束重建技术研究

在逆向工程中,针对测量数据点进行拉伸面和旋转面的参数提取并重建曲面是非常重要的.轮廓线是拉伸面和旋转面的重要参数,提出一种改进的轮廓数据排序及曲率估算方法.在此基础上,针对由直线和圆弧特征构成的轮廓数据提出依据弦转角和曲率差对轮廓数据分段.最后采取全局约束策略,通过对轮廓数据整体拟合来提取轮廓线.实验结果表明了该算法的有效性和实用性.     

A topological approach for surface reconstruction from sample points

Most algorithms for surface reconstruction from sample points rely on computationally demanding operations to derive the reconstruction. In this paper we introduce an innovative approach for generating 3D piecewise linear approximations from sample points that relies strongly on topological information, thus reducing the computational cost and numerical instabilities typically associated with geometric computations. Discrete Morse theory provides the basis for a topological framework that supports a robust reconstruction algorithm capable of handling multiple components and has low computational cost. We describe the proposed approach and introduce the reconstruction algorithm, called TSR – topological surface reconstructor. Some reconstruction results are presented and the performance of TSR is compared with that of other reconstruction approaches for some standard point sets.