Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models

Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.     

A sampling theorem for fractional S-transform with error estimation

Fractional S-transform (FrST) is generalization of the S-transform (ST). Over the past few years, FrST plays an active role in optics, communication, and signal processing applications. This paper is focused on the sampling theorem of FrST, which is based on multiresolution subspace. It will provide an appropriate and reasonable model of sampling and reconstruction of the signal for real applications. Moreover, two kinds of reconstruction error, estimate truncation and aliasing error for sampling are also discussed. Efficacy of proposed work is validated with simulation results.     

A new easy camera calibration technique based on circular points

Inspired by Zhang's work on flexible calibration technique, a new easy technique for calibrating a camera based on circular points is proposed. The proposed technique only requires the camera to observe a newly designed planar calibration pattern (referred to as the model plane hereinafter) which includes a circle and a pencil of lines passing through the circle's center, at a few (at least three) different unknown orientations, then all the five intrinsic parameters can be determined linearly. The main advantage of our new technique is that it needs to know neither any metric measurement on the model plane, nor the correspondences between points on the model plane and image ones, hence the whole calibration process becomes extremely simple. The proposed technique is particularly useful for those people who are not familiar with computer vision. Experiments with simulated data as well as with real images show that our new technique is robust and accurate.     

计算机视觉中摄像机定标综述

回顾了摄影测量学和计算机视觉中的各种摄像机定标方法,对各种方法进行了分析、比较、并讨论了摄像机定标方法应用于计算机视觉中的特点.     

Physical sketching: Reconstruction and analysis of 3D objects from freehand sketches

When designing a 3D object, designers, engineers and teachers often begin investigating potential design tradeoffs by creating informal sketches. Ideally, these sketches-in combination with a variety of engineering analysis tools-would allow prediction of the object’s physical properties, especially those that affect the critical early design process. We introduce a pen-based system that reconstructs 3D spatial geometry from a single 2D freehand-sketch consisting of straight and curved lines in interactive time. Several optimization-based approaches to this problem have been proposed, but these generally have difficulty converging to an acceptable solution because the dimensionality of the search space is large. The primary contribution of this paper is a new reconstruction algorithm for orthographic projections of 3D wireframes. The algorithm reconstructs the depths of each vertex by exploiting geometric regularities among the graph lines in a reduced solution space, then optimizes a cost function over this space to recover the vertex depths. A second optimization algorithm is used to infer the 3D geometry of curved strokes once the vertex depths have been recovered. The proposed approach can recover the geometry of several objects with approximately 50 curved strokes in near interactive time. We also present an iterative, Tablet-PC-based design system that uses the proposed reconstruction algorithm to recover 3D objects from 2D orthographic sketches. The system allows the reconstructed objects to be subjected to two types of physical analysis, the results of which are superimposed directly on the sketch: a fast, kinematic simulation, and a complete finite-element-based static analysis. The object can quickly be modified in place using the pen-based interface according to the results of the analysis to allow for iterative design work. We demonstrate the system in action on a variety of early-stage design analyses.     

Modeling errors for dimensional inspection using active vision

In active vision inspection, errors in dimensional measurements are often due to displacement of the sensor and image digitization effects. Using a model of error in linear measurements based on a normally distributed sensor displacement, uniform image digitization and geometric approximation, the accuracy of measurements from a particular sensor setting can be assessed. In comparison with experimental measurements, this error model demonstrates a high predictive ability. Related experiments demonstrate a procedure for assessing the accuracy of sensor settings providing insight into what sensor settings lead to lower measurement variances and thus higher measurement reliability.     

计算机视觉系统框架结构研究

计算机视觉系统框架的研究对计算机视觉是十分重要的,它指导着计算机视觉的发展方向。文章对计算机视觉发展几十年来在系统结构框架方面(主要是三维重建理论框架和主动视觉理论框架)所取得的研究进行了回顾和总结,并在前人所取得的成果之上,根据现有的实际情况,对原有框架结构进行了一些补充,同时对计算机视觉系统框架提出一些新的看法。     

基于模型的单视图线性自标定及三维重建算法

通过建立深度矢量所在的零空间和目标模型矩阵的零空间之间的关系,开发了一个从目标单视图线性地精确复原其三维位姿的新算法,该算法只需要6个特征点即可实现目标三维位姿的线性重建。理论分析和实验数据表明,该算法具有快速高效、简单实用、抗噪能力较强的优点。     

一种基于三维骨架化的人体姿势估计方法

提出了一种基于三维2 步子迭代混合细化算法和启发式搜索的无标记三维人体姿势估计方法.其主要思想是首先通过一种新的2 步子迭代混合细化算法,对由Shape-from-Silhouette(SFS)算法从多幅二维图像中重建出的人体三维模型进行骨架化,然后通过一种启发式的方法进行模型匹配.实验结果表明,该方法不仅思想简单,而且具有较好的鲁棒性,能够很好的去除各种噪声的影响.     

深度学习单目深度估计研究进展

单目深度估计是从单幅图像中获取场景深度信息的重要技术,在智能汽车和机器人定位等领域应用广泛,具有重要的研究价值。随着深度学习技术的发展,涌现出许多基于深度学习的单目深度估计研究,单目深度估计性能也取得了很大进展。本文按照单目深度估计模型采用的训练数据的类型,从3个方面综述了近年来基于深度学习的单目深度估计方法:基于单图像训练的模型、基于多图像训练的模型和基于辅助信息优化训练的单目深度估计模型。同时,本文在综述了单目深度估计研究常用数据集和性能指标基础上,对经典的单目深度估计模型进行了性能比较分析。以单幅图像作为训练数据的模型具有网络结构简单的特点,但泛化性能较差。采用多图像训练的深度估计网络有更强的泛化性,但网络的参数量大、网络收敛速度慢、训练耗时长。引入辅助信息的深度估计网络的深度估计精度得到了进一步提升,但辅助信息的引入会造成网络结构复杂、收敛速度慢等问题。单目深度估计研究还存在许多的难题和挑战。利用多图像输入中包含的潜在信息和特定领域的约束信息,来提高单目深度估计的性能,逐渐成为了单目深度估计研究的趋势。     

Bolstered error estimation

We propose a general method for error estimation that displays low variance and generally low bias as well. This method is based on “bolstering” the original empirical distribution of the data. It has a direct geometric interpretation and can be easily applied to any classification rule and any number of classes. This method can be used to improve the performance of any error-counting estimation method, such as resubstitution and all cross-validation estimators, particularly in small-sample settings. We point out some similarities shared by our method with a previously proposed technique, known as smoothed error estimation. In some important cases, such as a linear classification rule with a Gaussian bolstering kernel, the integrals in the bolstered error estimate can be computed exactly. In the general case, the bolstered error estimate may be computed by Monte-Carlo sampling; however, our experiments show that a very small number of Monte-Carlo samples is needed. This results in a fast error estimator, which is in contrast to other resampling techniques, such as the bootstrap. We provide an extensive simulation study comparing the proposed method with resubstitution, cross-validation, and bootstrap error estimation, for three popular classification rules (linear discriminant analysis, k-nearest-neighbor, and decision trees), using several sample sizes, from small to moderate. The results indicate the proposed method vastly improves on resubstitution and cross-validation, especially for small samples, in terms of bias and variance. In that respect, it is competitive with, and in many occasions superior to, bootstrap error estimation, while being tens to hundreds of times faster. We provide a companion web site, which contains: (1) the complete set of tables and plots regarding the simulation study, and (2) C source code used to implement the bolstered error estimators proposed in this paper, as part of a larger library for classification and error estimation, with full documentation and examples. The companion web site can be accessed at the URL http://ee.tamu.edu/~edward/bolster.     

一种新颖实用的基于视觉导航的三维重建算法

根据立体视觉在视觉导航应用中的特点，提出了由视差图重建三维场景的整套算法。首先，推导视差图里各点在摄像机坐标系下的三维坐标。其次，建立摄像机坐标系与车体坐标系的转换关系，得到更有实际意义的在车体坐标系下各点的三维坐标。最后，通过分格显示地貌的算法，大大减少了数据量。实验结果表明，该算法计算量小、鲁棒性强，完全满足视觉导航中的实时性、准确性要求。     

基于立体视觉的水下三维测量系统研究

随着我国海洋战略的提出,对于海洋观测技术和装备的需求日趋迫切。针对现有水下成像系统无法实现精确三维测量这一难题,该文提出了一种基于双目立体视觉原理的水下三维测量系统研究方法,并对其可行性进行了验证。针对水下成像过程存在的水体界面折射问题,该文提出了相应的相机成像模型及系统参数标定方法,建立了防水深度达 30 m 的双目水下测量及照明装置,并在水池、近海条件下进行了实地测试。实验结果显示,在水体条件较好的情况下,系统观测距离可达 8 m 以上,有效测量距离为 0.5～4.5 m,在 0.5 m 和 4.5 m 距离处的测量误差分别为 2 mm 和 20 mm。实验验证了 水下双目成像模型、立体标定、测量模型等方法的有效性和精确性,可为水下检修作业等海洋工程行业提供一种有效的三维测量技术手段。     

一种基于视沉的增强现实三维注册算法

为了提高增强现实系统三维注册的精度和效率，提出了一种基于计算机视觉的增强现实三维注射算法，并已将其用于采用透射式头盔显示器的增强现实系统中，该算法有如下特点：（1）构架简单，实用性强，一般情况下只需4个平面标志物就可实现三维注册；（2）工作范围大，甚至可以应用到室外的增强现实系统中；（30数值求解过程最线性过程，误差小，可以满足增强现实系统高精度三维注册的要求，另外，应用这种算法所需要作的图象处理计算量很小，其典型系统包括一个彩色CCD和几个不同颜色的标志点，由于很容易获得标志点的对应像面位置，而且不需要计算图象对，因此实时性好，是一般图形工作站和PC机上的增强现实系统进行实时注册的算法基础。     

Multiview 3D reconstruction of the archaeological site at Weymouth from image series

Multiview (n-view or multiple view) 3D reconstruction is the computationally complex process by which a full 3D model is derived from a series of overlapping images. It is based on research in the field of computer vision, which in turn relies on older methods from photogrammetry. This report presents a multiview reconstruction tool chain composed from various freely available, open source components and a practical application example in the form of a 3D model of an archaeological site.     

一种基于主动视觉的三维结构恢复和直接欧氏重建算法

利用三正交平移运动, 提出了一种三维结构恢复和直接欧氏重建新算法. 算法仅需利用主动视觉平台控制相机作一组三正交平移运动, 然后通过图像对应点和平移运动的距离就可以恢复平面结构信息和进行欧氏重建. 并且无需假定相机畸变因子为零. 算法计算过程中无需求解相机的内参数, 也无需进行分层重构, 它是一种直接的欧氏重建算法, 避免了传统算法中的相机标定、仿射重建等两大难题, 并且计算过程完全线性化, 简单实用. 最后用模拟实验和真实图像实验对算法进行验证, 实验结果表明了算法的有效性和准确性.     

基于视觉导航的三维重建算法误差分析及补偿

根据视觉导航应用的特点,提出了由视差图重建三维场景算法的误差分析及误差补偿算法.首先介绍立体视觉三维重建方法.其次针对摄像机透视投影的针孔模型,分析三维重建误差的主要来源.最后针对误差来源提出了误差分析及补偿方法.实验结果证明,此方法计算量适中,完全满足了视觉导航中的实时性、准确性的要求.     

Successively alternate least square for low-rank matrix factorization with bounded missing data

The problem of low-rank matrix factorization with missing data has attracted many significant attention in the fields related to computer vision. The previous model mainly minimizes the total errors of the recovered low-rank matrix on observed entries. It may produce an optimal solution with less physical meaning. This paper gives a theoretical analysis of the sensitivities of the original model and proposes a modified constrained model and iterative methods for solving the constrained problem. We show that solutions of original model can be arbitrarily far from each others. Two kinds of sufficient conditions of this catastrophic phenomenon are given. In general case, we also give a low bound of error between an ?-optimal solution that is practically obtained in computation and a theoretically optimal solution. A constrained model on missing entries is considered for this missing data problem. We propose a two-step projection method for solving the constrained problem. We also modify the method by a successive alternate technique. The proposed algorithm, named as SALS, is easy to implement, as well as converges very fast even for a large matrix. Numerical experiments on simulation data and real examples are given to illuminate the algorithm behaviors of SALS.     

A Unified Theory of Uncalibrated Stereo for Both Perspective and Affine Cameras

This paper addresses the recovery of structure and motion from uncalibrated images of a scene under full perspective or under affine projection. Particular emphasis is placed on the configuration of two views, while the extension to $N$ views is given in Appendix. A unified expression of the fundamental matrix is derived which is valid for any projection model without lens distortion (including full perspective and affine camera). Affine reconstruction is considered as a special projective reconstruction. The theory is elaborated in a way such that everyone having knowledge of linear algebra can understand the discussion without difficulty. A new technique for affine reconstruction is developed, which consists in first estimating the affine epipolar geometry and then performing a triangulation for each point match with respect to an implicit common affine basis.     

Evaluating the risks of computer fraud and error

Computer systems now dominate the processing of financial information. In many cases these systems are very sophisticated and provide people with direct access to an organisation's financial information. This paper proposes a new approach to evaluating the risks of fraud and error in such computer systems. This approach is efficient to use, precise in its conclusions and has already been widely tested in practice.