Using geometric constraints through parallelepipeds for calibration and 3D modeling

In this paper we present a system for the reconstruction of 3D models of architectural scenes from single or multiple uncalibrated images. The partial 3D model of a building is recovered from a single image using geometric constraints such as parallelism and orthogonality, which are likely to be found in most architectural scenes. The approximate corner positions of a building are selected interactively by a user and then further refined automatically using Hough transform. The relative depths of the corner points are calculated according to the perspective projection model. Partial 3D models recovered from different viewpoints are registered to a common coordinate system for integration. The 3D model registration process is carried out using modified ICP (iterative closest point) algorithm with the initial parameters provided by geometric constraints of the building. The integrated 3D model is then fitted with piecewise planar surfaces to generate a more geometrically consistent model. The acquired images are finally mapped onto the surface of the reconstructed 3D model to create a photo-realistic model. A working system which allows a user to interactively build a 3D model of an architectural scene from single or multiple images has been proposed and implemented.

In this paper we present a system for the reconstruction of 3D models of architectural scenes from single or multiple uncalibrated images. The partial 3D model of a building is recovered from a single image using geometric constraints such as parallelism and orthogonality, which are likely to be found in most architectural scenes. The approximate corner positions of a building are selected interactively by a user and then further refined automatically using Hough transform. The relative depths of the corner points are calculated according to the perspective projection model. Partial 3D models recovered from different viewpoints are registered to a common coordinate system for integration. The 3D model registration process is carried out using modified ICP (iterative closest point) algorithm with the initial parameters provided by geometric constraints of the building. The integrated 3D model is then fitted with piecewise planar surfaces to generate a more geometrically consistent model. The acquired images are finally mapped onto the surface of the reconstructed 3D model to create a photo-realistic model. A working system which allows a user to interactively build a 3D model of an architectural scene from single or multiple images has been proposed and implemented.

This paper presents a new linear framework to obtain 3D scene reconstruction and camera calibration simultaneously from uncalibrated images using scene geometry. Our strategy uses the constraints of parallelism, coplanarity, colinearity, and orthogonality. These constraints can be obtained in general man-made scenes frequently. This approach can give more stable results with fewer images and allow us to gain the results with only linear operations. In this paper, it is shown that all the geometric constraints used in the previous works performed independently up to now can be implemented easily in the proposed linear method. The study on the situations that cannot be dealt with by the previous approaches is also presented and it is shown that the proposed method being able to handle the cases is more flexible in use. The proposed method uses a stratified approach, in which affine reconstruction is performed first and then metric reconstruction. In this procedure, the additional constraints newly extracted in this paper have an important role for affine reconstruction in practical situations.

In this paper, efficient and generic tools for calibration and 3D reconstruction are presented. These tools exploit geometric constraints frequently present in man-made environments and allow camera calibration as well as scene structure to be estimated with a small amount of user interactions and little a priori knowledge. The proposed approach is based on primitives that naturally characterize rigidity constraints: parallelepipeds. It has been shown previously that the intrinsic metric characteristics of a parallelepiped are dual to the intrinsic characteristics of a perspective camera. Here, we generalize this idea by taking into account additional redundancies between multiple images of multiple parallelepipeds. We propose a method for the estimation of camera and scene parameters that bears strongsimilarities with some self-calibration approaches. Takingin to account prior knowledge on scene primitives or cameras, leads to simpler equations than for standard self-calibration, and is expected to improve results, as well as to allow structure and motion recovery in situations that are otherwise under-constrained. These principles are illustrated by experimental calibration results and several reconstructions from uncalibrated images.

Abstract. With the development of real 3D model production technology and the expansion of application field, people pay more and more attention to the quality of real 3D model. However, how to measure the quality of today's real 3D model have been bothering its producers and users. In this paper, we analysed the quality model of real scene 3D model based on oblique photography from the perspective of the third party. Our analysis is guided by the application requirements of real scene 3D model, combined with the existing production technology level. Our analysis is guided by the application requirements of real scene 3D model, combined with the existing production technology level, we established the quality framework of real scene 3D model. This quality framework of real 3D model includes nine quality elements. Using this quality framework, we made a quality evaluation test in Yingjing County, Sichuan Provence. The test results show that the quality framework can fully reflect the quality of the real scene 3D model. The quality framework of real scene 3D model established in this paper solves the problem that it is difficult to evaluate the quality of real scene 3D model. The quality framework provides a basis for comprehensive and objective evaluation of real scene 3D model quality.

3D reconstruction of complex geological bodies: Examples from the Alps

The three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and three-dimensional models are publicly available.

Camera calibration is a key component of three-dimensional particle tracking velocimetry (PTV) experiments, and its proper implementation is key to the success of the method. In this paper, we review and compare four different camera calibration models used in PTV experiments without volumetric refinement. One of the calibration models is new and provides an analytical inversion of the Soloff polynomial. The other three calibration models are taken from three established open source PTV frameworks: OpenPTV, MyPTV and proPTV. In particular, we present a general formulation of calibration models that allows their rigorous comparison and evaluation with respect to their 3D-to-2D projection errors and 2D-to-3D reconstruction errors. We compare the models and the calibration errors in three different tasks, including extrapolation and interpolation of marker points, using a realistic calibration of an experimental camera setup. In the end, we conclude with the pros and cons of each method in order to be able to choose the most suitable one for individual needs.

With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo vision system based on two-dimensional image modeling technology. The system can be divided into image processing, camera calibration, stereo matching, three-dimensional point reconstruction, and model reconstruction. In the part of image processing, common image processing methods, feature point extraction algorithm, and edge extraction algorithm are studied. On this basis, interactive local corner extraction algorithm and interactive local edge detection algorithm are proposed. It is found that the Harris algorithm can effectively remove the features of less information and easy to generate clustering phenomenon. At the same time, the method of limit constraints is used to match the feature points extracted from the image. This method has high matching accuracy and short time. The experimental research has achieved good matching results. Using the platform of binocular stereo vision system, each step in the process of three-dimensional reconstruction has achieved high accuracy, thus achieving the three-dimensional reconstruction of the target object. Finally, based on the research of three-dimensional reconstruction of mechanical parts and the designed binocular stereo vision system platform, the experimental results of edge detection, camera calibration, stereo matching, and three-dimensional model reconstruction in the process of three-dimensional reconstruction are obtained, and the full text is summarized, analyzed, and prospected.

We present methods for creating 3D graphical models of scenes from a limited numbers of images, i.e. one or two, in situations where no scene co‐ordinate measurements are available. The methods employ constraints available from geometric relationships that are common in architectural scenes – such as parallelism and orthogonality – together with constraints available from the camera. In particular, by using the circular points of a plane simple, linear algorithms are given for computing plane rectification, plane orientation and camera calibration from a single image. Examples of image based 3D modelling are given for both single images and image pairs.

The demand for 3D models that represent real-world objects such as structures and buildings has increased in recent years. It is becoming increasingly important that the reconstructions are not only visually convincing but also feature high geometric accuracy. This includes, for example, the fields of civil engineering, terrestrial surveying and archeology, where precise measurements are made in the models for documentation and analysis purposes. There are different approaches to create such a reconstruction. The photogrammetric method Structure from Motion and laser scanning are among the most widely used methods here, as they do not require a complicated setup and can be used for scenarios at small to large scale. Recent developments are enabling unmanned robotic systems, especially sensor mounted UAVs, to assist in the recording of areas which are otherwise difficult to observe. The demand for a high geometric accuracy, however, comes at the expense of high computational complexity of up to several days. Hence, especially real-time reconstructions are unfeasible, such that recording and reconstruction procedure must be executed consecutively. The resulting model quality, i.e. completeness and accuracy, is only assessable afterwards. Since it is often difficult or even impossible to improve these models with additional measurements afterwards, methods that ensure a reliable acquisition of sufficient data is required.
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\nIn this thesis we develop new methods and theory that address this problem for the mentioned sensor types. For both, a probabilistic description of the expected surface reconstruction error is maintained cost-efficiently as an estimate for the model quality during the recording procedure. For image sensors this is realized by incrementally constructing confidence ellipsoids that describe the information obtained from all views. With depth sensors the surface quality is described by the variance of a Gaussian process implicit surface regression fit to point cloud data using polyharmonic kernel functions. Sensor poses are then assessed by the information they add to the subsequent reconstruction up to a desired geometric accuracy using a formulation that is motivated from Optimal Experimental Design. This quantity is further used in an iterative next-best-view selection framework as a subproblem of a coverage path planning problem.
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\nThe general formulations presented in this thesis enables a wide range of applications, such as offline and online view planning or various autonomous robot systems under consideration of dynamic and geometric constraints. We present the first multi-view coverage path planning approach, specifically targeted at autonomous Structure from Motion data acquisition. Its correctness is validated in simulation using the physics simulator Gazebo. Furthermore, we lay a foundation for similar applications with depth sensors. All presented algorithms were developed with scalability in mind and show promising results regarding real-time usability.

The EiC and Associate EiC express our gratitude to David Forsyth, Brendan Frey, Venu Govindaraju, and Cordelia Schmid who are retiring as associate editors of TPAMI. While we will miss their dedication to the transactions, we hope that they will be enjoying a bit more free time. We are also pleased to announce that Professor Daniel Lopresti, Professor B.S. Manjunath, Professor Marc Pollefeys, and Professor Ramin Zabih have joined the editorial board. Professor Lopresti will oversee papers in document and handwriting analysis, biometrics, approximate string matching algorithms, and performance evaluation. Professor Manjunath will be considering papers in feature extraction, segmentation, image/video retrieval, and image registration. Professor Pollefeys will be responsible for submissions in structure from motion, stereo, multiple view geometry and camera calibration, 3D and appearance modeling, shape-from-X techniques, and novel sensors. Professor Zabih will handle papers on stereo and medical imaging as well as energy minimization and graph algorithms. We look forward to working with them. Their brief biographies appear herein.

The EiC and Associate EiC express our gratitude to David Forsyth, Brendan Frey, Venu Govindaraju, and Cordelia Schmid who are retiring as associate editors of TPAMI. While we will miss their dedication to the transactions, we hope that they will be enjoying a bit more free time. We are also pleased to announce that Professor Daniel Lopresti, Professor B.S. Manjunath, Professor Marc Pollefeys, and Professor Ramin Zabih have joined the editorial board. Professor Lopresti will oversee papers in document and handwriting analysis, biometrics, approximate string matching algorithms, and performance evaluation. Professor Manjunath will be considering papers in feature extraction, segmentation, image/video retrieval, and image registration. Professor Pollefeys will be responsible for submissions in structure from motion, stereo, multiple view geometry and camera calibration, 3D and appearance modeling, shape-from-X techniques, and novel sensors. Professor Zabih will handle papers on stereo and medical imaging as well as energy minimization and graph algorithms. We look forward to working with them. Their brief biographies appear herein.

The EiC and Associate EiC express our gratitude to David Forsyth, Brendan Frey, Venu Govindaraju, and Cordelia Schmid who are retiring as associate editors of TPAMI. While we will miss their dedication to the transactions, we hope that they will be enjoying a bit more free time. We are also pleased to announce that Professor Daniel Lopresti, Professor B.S. Manjunath, Professor Marc Pollefeys, and Professor Ramin Zabih have joined the editorial board. Professor Lopresti will oversee papers in document and handwriting analysis, biometrics, approximate string matching algorithms, and performance evaluation. Professor Manjunath will be considering papers in feature extraction, segmentation, image/video retrieval, and image registration. Professor Pollefeys will be responsible for submissions in structure from motion, stereo, multiple view geometry and camera calibration, 3D and appearance modeling, shape-from-X techniques, and novel sensors. Professor Zabih will handle papers on stereo and medical imaging as well as energy minimization and graph algorithms. We look forward to working with them. Their brief biographies appear herein.

Image Analysis for 3D Modeling, Rendering, and Virtual View Generation