General Dynamic Scene Reconstruction from Multiple View Video

This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques for dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance

HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

Review of research in feature-based design

Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made

GIS-3D Platform to Help Decision Making for Energy Rehabilitation in Urban Environments

One of the main current challenges of European cities is to become energy self-sufficient entities. One of the vectors for this challenge is to improve the energy efficiency of the buildings and to promote the generation of renewable energies in the urban environment. The article describes a tool based on GIS-3D technologies to support the identification of the energy rehabilitation potential of neighbourhoods based on the introduction of renewable energies. The platform is based on a urban 3D model that collects the geometry of buildings, together with relevant information for the identification of rehabilitation opportunities (e.g. surfaces, heights, orientations and slopes). The project includes the generation of a cloud-based repository, which incorporates active and passive innovative solutions with metrics that allow the comparison of the solutions and the applicability of them to the real environment. The identification of rehabilitation opportunities combines information resulting from the diagnosis of the current energy performance of the district's buildings with the potential for renewable generation in the area. A multicriteria analysis process facilitates the identification of the most appropriate rehabilitation solutions for the analysed environment based on different criteria as energy, cost or applicability. The result can be visualized through a web tool that combines 2D and 3D information, with comparative information in a quantitative and geo-referenced manner. The flexibility of the architecture allows the application of the same approach to different urban challenges as the application of energy conservation measures to protected historic urban areas.The work of this paper has been done as part of the projects RE3D “Energy Rehabilitation in 3D” and RE2H “Energy Retrofitting of Historic Districts”, both partially funded by Basque Government, with references ZL-2017/00998 and ZL-2017/00981 respectively

Solar stereoscopy - where are we and what developments do we require to progress?

Observations from the two STEREO-spacecraft give us for the first time the possibility to use stereoscopic methods to reconstruct the 3D solar corona. Classical stereoscopy works best for solid objects with clear edges. Consequently an application of classical stereoscopic methods to the faint structures visible in the optically thin coronal plasma is by no means straight forward and several problems have to be treated adequately: 1.)First there is the problem of identifying one dimensional structures -e.g. active region coronal loops or polar plumes- from the two individual EUV-images observed with STEREO/EUVI. 2.) As a next step one has the association problem to find corresponding structures in both images. 3.) Within the reconstruction problem stereoscopic methods are used to compute the 3D-geometry of the identified structures. Without any prior assumptions, e.g., regarding the footpoints of coronal loops, the reconstruction problem has not one unique solution. 4.) One has to estimate the reconstruction error or accuracy of the reconstructed 3D-structure, which depends on the accuracy of the identified structures in 2D, the separation angle between the spacecraft, but also on the location, e.g., for east-west directed coronal loops the reconstruction error is highest close to the loop top. 5.) Eventually we are not only interested in the 3D-geometry of loops or plumes, but also in physical parameters like density, temperature, plasma flow, magnetic field strength etc. Helpful for treating some of these problems are coronal magnetic field models extrapolated from photospheric measurements, because observed EUV-loops outline the magnetic field. This feature has been used for a new method dubbed 'magnetic stereoscopy'. As examples we show recent application to active region loops.Comment: 12 Pages, 9 Figures, a Review articl

DLR Contribution to the First High Lift Prediction Workshop

DLR’s contribution to the first AIAA High Lift Prediction Workshop (HiLiftPW-1) covers computations of all three scheduled test cases for the NASA trapezoidal wing in high lift configuration. The DLR finite volume code TAU has been employed as the flow solver. In a standard set-up the one-equation turbulence model of Spalart and Allmaras in the original formulation is used to model effects of turbulence. For selected grids and flow conditions, the k-ω SST model of Menter and a differential Reynolds stress model (SSG/LLR-ω ) developed by DLR have been considered. DLR contributed with two hybrid unstructured grid families to the workshop. The grids have been generated with the grid generation packages Centaur and Solar. A grid family with three Solar grids has been generated and provided to the workshop featuring grids of 12·10^6 , 37·10^6 , and 111·10^6 points for test case 1. In addition, a Solar grid of 37·10^6 points has been provided for test case 2, and a grid of 40·10^6 for the configuration including the slat and flap brackets (test case 3). DLR didn’t succeed in generating a fine-grid with the Centaur package. In order to complete a Centaur grid family with three grid levels an extra-coarse grid has been provided. Thus, the three levels of the Centaur grid family are realized by grids of 13·10^6 , 16·10^6 , and 32·10^6 points. In general a go o d agreement between the experimental evidence and the polar computations on the Solar and Centaur grids is found in terms of forces, moments and wing pressure distributions. The wing tip area with the rearward part of the main wing and the flap represents the most challenging part of the configuration, especially at angles of attack around maximum lift. The deviations between the TAU solutions and the experimental data in this area are only weakly influenced by the different grid topologies or turbulence models used. The influence of the grid resolution of both grid families is comparable, taking into account the different absolute resolution levels of both grid families. Including the slat and flap brackets leads to the expected lift decrease. Concerning the convergence properties, a strong dependence on the numerical start-up procedure has been detected in many of the computations at higher angles of attack

Design project 1968/9: management report

1. INTRODUCTION The design of an automatic assembly machine with versatility in application was undertaken as a group project by post-graduate students attending a course in production technology. This report summarises the work clone and conclusions reached during the project. In addition there are available five other reports which describe the designing of different areas of the machine in full detail (refs. 1 to 6). There is also the report of a technical survey which was carried out to investigate industrial requirements for automatic assembly. In order that this report may serve as a guide, a summary of the content of each of the other reports is included

Learning Single-Image Depth from Videos using Quality Assessment Networks

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild

The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.Comment: 3DV 201