A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models

Given large amount of real photos for training, Convolutional neural network shows excellent performance on object recognition tasks. However, the process of collecting data is so tedious and the background are also limited which makes it hard to establish a perfect database. In this paper, our generative model trained with synthetic images rendered from 3D models reduces the workload of data collection and limitation of conditions. Our structure is composed of two sub-networks: semantic foreground object reconstruction network based on Bayesian inference and classification network based on multi-triplet cost function for avoiding over-fitting problem on monotone surface and fully utilizing pose information by establishing sphere-like distribution of descriptors in each category which is helpful for recognition on regular photos according to poses, lighting condition, background and category information of rendered images. Firstly, our conjugate structure called generative model with metric learning utilizing additional foreground object channels generated from Bayesian rendering as the joint of two sub-networks. Multi-triplet cost function based on poses for object recognition are used for metric learning which makes it possible training a category classifier purely based on synthetic data. Secondly, we design a coordinate training strategy with the help of adaptive noises acting as corruption on input images to help both sub-networks benefit from each other and avoid inharmonious parameter tuning due to different convergence speed of two sub-networks. Our structure achieves the state of the art accuracy of over 50\% on ShapeNet database with data migration obstacle from synthetic images to real photos. This pipeline makes it applicable to do recognition on real images only based on 3D models.Comment: 14 page

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction

CYBERSAR

The project aims at setting up an advanced cyberinfrastructure based on dedicated optical networks to support collaborative research application. The aim of Cybersar computational infrastructure is to support innovative computational applications by using leading edge hardware and technological solutions and to provide an experimental platform for research on the enabling technologies that will power next generation cyberinfrastructures. In particular, in the Visual Computing Group, we study techniques for processing and rendering very large scale 3D datasets on innovative large scale displays.Completato€ 1.291.50

Immersive and non immersive 3D virtual city: decision support tool for urban sustainability

Sustainable urban planning decisions must not only consider the physical structure of the urban development but the economic, social and environmental factors. Due to the prolonged times scales of major urban development projects the current and future impacts of any decision made must be fully understood. Many key project decisions are made early in the decision making process with decision makers later seeking agreement for proposals once the key decisions have already been made, leaving many stakeholders, especially the general public, feeling marginalised by the process. Many decision support tools have been developed to aid in the decision making process, however many of these are expert orientated, fail to fully address spatial and temporal issues and do not reflect the interconnectivity of the separate domains and their indicators. This paper outlines a platform that combines computer game techniques, modelling of economic, social and environmental indicators to provide an interface that presents a 3D interactive virtual city with sustainability information overlain. Creating a virtual 3D urban area using the latest video game techniques ensures: real-time rendering of the 3D graphics; exploitation of novel techniques of how complex multivariate data is presented to the user; immersion in the 3D urban development, via first person navigation, exploration and manipulation of the environment with consequences updated in real-time. The use of visualisation techniques begins to remove sustainability assessment’s reliance on the existing expert systems which are largely inaccessible to many of the stakeholder groups, especially the general public

Advances in massive model visualization in the CYBERSAR project

We provide a survey of the major results obtained within the CYBERSAR project in the area of massive data visualization. Despite the impressive improvements in graphics and computational hardware performance, interactive visualization of massive models still remains a challenging problem. To address this problem, we developed methods that exploit the programmability of latest generation graphics hardware, and combine coarse-grained multiresolution models, chunk-based data management with compression, incremental view-dependent level-of-detail selection, and visibility culling. The models that can be interactively rendered with our methods range from multi-gigabyte-sized datasets for general 3D meshes or scalar volumes, to terabyte-sized datasets in the restricted 2.5D case of digital terrain models. Such a performance enables novel ways of exploring massive datasets. In particular, we have demonstrated the capability of driving innovative light field displays able of giving multiple freely moving naked-eye viewers the illusion of seeing and manipulating massive 3D objects with continuous viewer-independent parallax.233-23

LIME : Software for 3-D visualization, interpretation, and communication of virtual geoscience models

Parts of LIME have been developed to address research requirements in projects funded by the Research Council of Norway (RCN) through the Petromaks and Petromaks 2 programs. The following grants are acknowledged: 153264 (VOG [Virtual Outcrop Geology]; with Statoil ASA), 163316 (Carbonate Reservoir Geomodels [IRIS (International Research Institute of Stavanger)]), 176132 (Paleokarst Reservoirs [Uni Research CIPR]), 193059 (EUSA; with FORCE Sedimentology and Stratigraphy Group), 234152 (Trias North [University of Oslo]; with Deutsche Erdoel AG, Edison, Lundin, Statoil, and Tullow), 234111 (VOM2MPS [Uni Research CIPR]; with FORCE Sedimentology and Stratigraphy Group), as well as SkatteFUNN (RCN) project 266740. In addition, the SAFARI project consortium (http://safaridb.com) is thanked for its continued support. The OSG and wxWidgets communities are acknowledged for ongoing commitment to providing mature and powerful software libraries. All authors thank colleagues past and present for studies culminating in the presented figures: Kristine Smaadal and Aleksandra Sima (Figs. 1 and 4); Colm Pierce (Fig. 2A); Eivind Bastesen, Roy Gabrielsen and Haakon Fossen (Fig. 3); Christian Haug Eide (Fig. 7); Ivar Grunnaleite and Gunnar Sælen (Fig. 8); and Magda Chmielewska (Fig. 9). Isabelle Lecomte contributed to discussions on geospatial-geophysical data fusion. Bowei Tong and Joris Vanbiervliet are acknowledged for internal discussions during article revision. The lead author thanks Uni Research for providing a base funding grant to refine some of the presented features. Finally, authors Buckley and Dewez are grateful to Institut Carnot BRGM for the RADIOGEOM mobility grant supporting the writing of this paper. Corbin Kling and one anonymous reviewer helped improve the final manuscript.Peer reviewedPublisher PD

Coarse-grained Multiresolution Structures for Mobile Exploration of Gigantic Surface Models

We discuss our experience in creating scalable systems for distributing and rendering gigantic 3D surfaces on web environments and common handheld devices. Our methods are based on compressed streamable coarse-grained multiresolution structures. By combining CPU and GPU compression technology with our multiresolution data representation, we are able to incrementally transfer, locally store and render with unprecedented performance extremely detailed 3D mesh models on WebGL-enabled browsers, as well as on hardware-constrained mobile devices