The photorealistic acquisition of 3D objects often requires color information from digital photography to be mapped on the acquired geometry, in order to obtain a textured 3D model. This paper presents a novel fully automatic 2D/3D global registration pipeline consisting of several stages that simultaneously register the input image set on the corresponding 3D object. The first stage exploits Structure From Motion (SFM) on the image set in order to generate a sparse point cloud. During the second stage, this point cloud is aligned to the 3D object using an extension of the 4 Point Congruent Set (4PCS) algorithm for the alignment of range maps. The extension accounts for models with different scales and unknown regions of overlap. In the last processing stage a global refinement algorithm based on mutual information optimizes the color projection of the aligned photos on the 3D object, in order to obtain high quality textures. The proposed registration pipeline is general, capable of dealing with small and big objects of any shape, and robust. We present results from six real cases, evaluating the quality of the final colors mapped onto the 3D object. A comparison with a ground truth dataset is also presented
This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D structure from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.
We describe an automated system for detecting, localising, clustering and ranking visual changes on tunnel surfaces. The system is designed to provide assistance to expert human inspectors carrying out structural health monitoring and maintenance on ageing tunnel networks. A three-dimensional tunnel surface model is first recovered from a set of reference images using Structure from Motion techniques. New images are localised accurately within the model and changes are detected versus the reference images and model geometry. We formulate the problem of detecting changes probabilistically and evaluate the use of different feature maps and a novel geometric prior to achieve invariance to noise and nuisance sources such as parallax and lighting changes. A clustering and ranking method is proposed which efficiently presents detected changes and further improves the inspection efficiency. System performance is assessed on a real data set collected using a low-cost prototype capture device and labelled with ground-truth. Results demonstrate that The authors gratefully acknowledge the support by Toshiba Research Europe.
We describe a system for the detection of changes in multiple views of a textured surface taken at different times by a moving camera. Our motivation is the development of a non-contact inspection system, summarised in fig. 1, to be used for detecting anomalous visual changes on surfacesin this case on concrete tunnel linings. This application is of increasing social importance as tunnels and other large-scale infrastructure age and more efficient methods for structural inspection are required to allow their continued safe operation.The problem is challenging for several reasons: (i) Size and nature of changes. Changes of interest are often small and subtle -e.g. a fattening in the width of a hairline crack or a patch of discolouration caused by organic growth or surface damage. (ii) Nuisance factors. A sizeable proportion of the observed change over time is caused by nuisance factors, either internal to the acquisition system (such as different image sensors, capture settings or lighting setup) or due to external causes (for example, seasonal changes of temperature and humidity). (iii) Registration error. Achieving the pixel-accurate registration typically required for change detection is challenging because neither the sensor positions nor the tunnel geometry can be reliably determined. Parallax errors are common.We address these challenges by first using a structure-from-motion pipeline to approximately register images from our robotic inspection rig to the reconstructed surface of interest. Given a pair of registered image patches from different times, our main contribution is a novel approach to detect changes between the patches using a two-channel convolutional neural network (CNN). CNNs have recently been shown to be very effective at learning invariance to certain modes of image variability, but require large amounts of labelled image data to train. We create an unlimited source of negative pairs (i.e. patches where no abnormal change has occurred) by taking registered viewpoints from different cameras from the same time. We supplement this with a smaller dataset of negative pairs across the different test times from regions where no changes of interest have occurred. This requires a limited effort in coarsely labelling a small subset of the test data. Together, these negative pairs capture much of the natural variance from nuisance factors and registration error. For the positive (changed) pair generation, we provide randomly sampled pairs as well as synthetically generated changes using a crack model (fig. 2). The homogeneity of the tunnel environment allows a network to generalize well from a manageable amount of labelled ground-truth data.Our approach is similar to [1], who learn a two-channel CNN for the inverse problem of similarity measurement between image patch pairs. A key difference is that we train directly on a mixture of synthetic data and task data generated by our own pipeline, allowing us to learn task-specific invariances for the improved detection of changes.We evaluate our system usi...
Abstract-We propose a system for performing structural change detection in street-view videos captured by a vehiclemounted monocular camera over time. Our approach is motivated by the need for more frequent and efficient updates in the large-scale maps used in autonomous vehicle navigation. Our method chains a multi-sensor fusion SLAM and fast dense 3D reconstruction pipeline, which provide coarsely registered image pairs to a deep deconvolutional network for pixel-wise change detection. To train and evaluate our network we introduce a new urban change detection dataset which is an order of magnitude larger than existing datasets and contains challenging changes due to seasonal and lighting variations. Our method outperforms existing literature on this dataset, which we make available to the community, and an existing panoramic change detection dataset, demonstrating its wide applicability.
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