This article introduces how an exact computation library based on rational arithmetic has been used in a polyhedral modeler based on face shifts and topological event detection. The goal of the use of exact computation is to get rid of the imprecision in the geometrical predicates computations, and thus to avoid false positives and false negatives in the topological events detection. This article also presents two algorithms which transform a polyhedral mesh with an approximated geometry (a mesh with faces which does not co-intersect in one point) into a mesh with the same structure, but with a non-approximate geometry. This is, to our knowledge, the first attempt to use rational arithmetic in a polyhedral modeler to manage the geometrical data. The reasons why rational arithmetic has not been used before are the memory consumption that it can generate, but also the fact that to keep an absolute precision, some operators and functions can not be used (square root, logarithm, trigonometric functions, etc.) and finally the fact that all data are produced using floating-point arithmetic, and so that data should be corrected before use. This article explains how all these issues have been handled.

Surface reflectance (SR) is essential for many remote sensing applications, but retrieving it from aerial images is challenging due to the lack of in-flight radiometric calibration and varying acquisition conditions. We propose a novel method for radiometric cross-calibration of aerial imagery using satellite Top-of-Atmosphere (TOA) reflectance. The method involves estimating at-sensor reflectance at the airborne altitude from satellite TOA reflectance, followed by spectral band adjustment and spatial alignment between satellite and airborne imagery. A linear radiometric model is derived to relate the Digital Number (DN) to the at-sensor reflectance from a selected subset of robust aerial-satellite pixel correspondences. The radiometric calibration parameter was retrieved using linear regression. The method is particularly suitable for airborne campaigns that lack onboard or in-situ radiometric calibration equipment. An ablation study is presented to analyze the selection of reliable reference pixels.

In the context of urban 3D mapping, the 3D modelling step is a crucial operation, which can be very error prone, particularly when high fidelity and accuracy are required. While automatic reconstruction tools are way faster and less expensive than manual ones, these last are less sensible to the data defects.To combine all this complementary advantages, we propose a semi-automatic approach, where human operators will manually correct the result of an automatic reconstruction tool.While the automated reconstruction tool will be taken on the shelf, we have developed our own polyhedral modeler tool to make the correction step. In this article, we present this polyhedral modeler, which is based on face shifting, edge flipping and automatic topological events resolution.

In this article, we present two online platforms developed for the structuring and valorization of old geographical iconographic collections: a multimodal search engine for their indexing, retrieval and interlinking, and a 3D navigation platform for their visualization in spatial context. In particular, we show how the joint use of these functionalities, guided by geolocation, brings structure and knowledge to the manipulated collections. In the demonstrator, they consist of 54,000 oblique aerial photographs from several French providers (national archives, a museum and a mapping agency).

Iconographic image collections are a cultural heritage that could reach a larger audience by proposing their immersive presentation in a 3D web application. Proposing a historical street view application, based on these historical images, raises issues such as the unavailability of historical 3D models of the scene and the heterogeneity and sparsity of these photographs. We propose to use the 3D city and terrain models of the current scene, as well as a 3D point cloud if available, to simultaneously reproject and blend many historical images using an image-based rendering approach. Our contributions raise significantly the number of projective textures blended per rendering pass (typically from 8 to 40) on triangular meshes (of the 3D city and terrain models) and on point clouds. As a first step to tackle diachrony artifacts, we also propose a simple point cloud classification to filter in the shader the points corresponding to building or terrain details from the points corresponding to transient objects.

Abstract. Iconographic representations, such as historical photos of geographic spaces, are precious cultural heritage resources capable of describing a particular geographical area’s evolution over time. These photographic collections may vary in size, between hundreds and thousands of items. With the advent of the digital era, many of these documents have been digitized, spatialized, and are available online. Browsing through these digital image collections represents new challenges. This paper examines the topic of historical image exploration in a virtual environment enabling the co-visualization of historical photos into a contemporary 3D scene. We address the topic of user interaction considering the potential volume of the input data. Our methodology is based on design guidelines that rely on visual perception techniques to ease visual complexity and improve saliency on specific cues. The designs are additionally implemented following an image-based rendering approach and evaluated in a group of users. Overall, these propositions may be a notable addition to creating innovative ways to visualize and discover historical images in a virtual geographic environment.

We present an out-of-core and distributed surface reconstruction algorithm which scales efficiently on arbitrarily large point clouds (with optical centres) and produces a 3D watertight triangle mesh representing the surface of the underlying scene. Surface reconstruction from a point cloud is a difficult problem and existing state of the art approaches are usually based on complex pipelines making use of global algorithms (i.e. Delaunay triangulation, graph-cut optimisation). For one of these approaches, we investigate the distribution of all the steps (in particular Delaunay triangulation and graph-cut optimisation) in order to propose a fully scalable method. We show that the problem can be tiled and distributed across a cloud or a cluster of PCs by paying a careful attention to the interactions between tiles and using Spark computing framework. We confirm the efficiency of this approach with an in-depth quantitative evaluation and the successful reconstruction of a surface from a very large data set which combines more than 350 million aerial and terrestrial LiDAR points.

In the mid-1950s, the first improvised huts for Algerian workers appeared in Nanterre. Soon, these informal shacks grew together and ended up forming urban complexes, presented and administered as shantytowns, and the city of Nanterre was then durably associated with them. This article proposes to review an interdisciplinary research experience around this object of study. Researchers in urban history and sociology collaborated with computer scientists from the IGN in order to use and enrich a platform for spatialisation and visualisation of heterogeneous data to document the history of these shantytowns and to understand the formation and permanence of these places in the current collective memory.

Abstract. In order to understand and explain urban climate, the visual analysis of urban climate data and their relationships with the urban morphology is at stake. This involves partly to co-visualize 3D field climate data, obtained from simulation, with urban 3D models. We propose two ways to visualize and navigate into simulated climate data in urban 3D models, using series of horizontal 2D planes and 3D point clouds. We then explore different parameters regarding transparency, 3D semiologic rules, filtering and animation functions in order to improve the visual analysis of climate data 3D distribution. To achieve this, we apply our propositions to the co-visualization of air temperature data with a 3D urban city model.

Urban climate data remain complex to analyze regarding their spatial distribution. The co-visualization of simulated air temperature into urban models could help experts to analyze horizontal and vertical spatial distributions. We design a co-visualization framework enabling simulated air temperature data exploration, based on the graphic representation of three types of geometric proxies, and their co-visualization with a 3D urban model with various possible rendering styles. Through this framework, we aim at allowing meteorological researchers to visually analyze and interpret the relationships between simulated air temperature data and urban morphology.

Abstract. This paper deals with the distributed computation of Delaunay triangulations of massive point sets, mainly motivated by the needs of a scalable out-of-core surface reconstruction workflow from massive urban LIDAR datasets. Such a data often corresponds to a huge point cloud represented through a set of tiles of relatively homogeneous point sizes. This will be the input of our algorithm which will naturally partition this data across multiple processing elements. The distributed computation and communication between processing elements is orchestrated efficiently through an uncentralized model to represent, manage and locally construct the triangulation corresponding to each tile. Initially inspired by the star splaying approach, we review the Tile& Merge algorithm for computing Distributed Delaunay Triangulations on the cloud, provide a theoretical proof of correctness of this algorithm, and analyse the performance of our Spark implementation in terms of speedup and strong scaling in both synthetic and real use case datasets. A HPC implementation (e.g. using MPI), left for future work, would benefit from its more efficient message passing paradigm but lose the robustness and failure resilience of our Spark approach.

This article presents a spatio-temporal web application dedicated to the co-exploitation of heterogeneous data spatialized in a common 3D environment, providing several paradigms for supporting their co-visualization and interactions within the 3D environment and across time. The relevance of this tool is demonstrated here with two use cases involving historians and sociologists with the common objective of better understanding the formation of the Parisian metropolis. The study focuses on the evolution of the city of Nanterre (Paris area), which underwent many changes in the 1950s, and in particular on shantytown areas. Through census as statistical data and aerial imagery as visual data, a group of historians and sociologists experimented the relevance of the joint exploitation of those heterogeneous data within the proposed spatio-temporal web application.

Archivists, historians and national mapping agencies, among others, are archiving large datasets of historical photographs. Nevertheless, the capturing devices used to acquire these images possessed a diversity of effects that influenced the quality of the final resulting picture, e.g. geometric distortion, chromatic aberration, depth of field variation, etc. This paper examines singularly the topic of geometric distortion for a co-visualization of historical photos within a 3D model of the photographed scene. A distortion function of an image is ordinarily estimated only on the image domain by adjusting its parameters to observations of point correspondences. This mathematical function may exhibit overfits, oscillations or may not be well defined outside of this domain. The contribution of this work is the description of a distortion model defined on the whole undistorted image plane. We extrapolate the distortion estimated only on the image domain and then transfer this distortion information to the view of the 3D scene. This enables to look at the scene through an estimated camera and zoom out to see the context around the original photograph with a well-defined and behaved distortion. These findings may be a significant addition to the overall purpose of creating innovative ways to examine and visualize old photographs.

This paper presents a fully automatic framework for the generation of so-called LiDAR orthoimages (i.e. 2D raster maps of the reflectance and height LiDAR samples) from ground-level LiDAR scans. Beyond the Digital Surface Model (DSM or heightmap) provided by the height orthoimage, the proposed method cost-effectively generates a reflectance channel that is easily interpretable by human operators without relying on any optical acquisition, calibration and registration. Moreover, it commonly achieves very high resolutions (1cm2 per pixel), thanks to the typical sampling density of static or mobile LiDAR scans. Compared to orthoimages generated from aerial datasets, the proposed LiDAR orthoimages are acquired from the ground level and thus do not suffer occlusions from hovering objects (trees, tunnels and bridges), enabling their use in a number of urban applications such as road network monitoring and management, as well as precise mapping of the public space e.g. for accessibility applications or management of underground networks. Its generation and usability however faces two issues : (i) the inhomogeneous sampling density of LiDAR point clouds and (ii) the presence of masked areas (holes) behind occluders, which include, in a urban context, cars, tree trunks, poles or pedestrians (i) is addressed by first projecting the point cloud on a 2D-pixel grid so as to generate sparse and noisy reflectance and height images from which dense images estimated using a joint anisotropic diffusion of the height and reflectance channels. (ii) LiDAR shadow areas are detected by analyzing the diffusion results so that they can be inpainted using an examplar-based method, guided by an alignment prior. Results on real mobile and static acquisition data demonstrate the effectiveness of the proposed pipeline in generating a very high resolution LiDAR orthoimage of reflectance and height while filling holes of various sizes in a visually satisfying way.

Estimating visibility in point clouds has many applications such as visualization, surface reconstruction and scene analysis through fusion of LiDAR point clouds and images. However, most current works rely on methods that require strong assumptions on the point cloud density, which are not valid for LiDAR point clouds acquired from mobile mapping systems, leading to low quality of point visibility estimations. This work presents a novel approach for the estimation of the visibility of a point cloud from a viewpoint. The method is designed to be fully automatic and it makes no assumption on the point cloud density. The visibility of each point is estimated by considering its screen-space neighborhood from the given viewpoint. Our results show that our approach succeeds better in estimating the visibility on real-world data acquired using LiDAR scanners. We evaluate our approach by comparing its results to a new manually annotated dataset, which we make available online.

Cet article de positionnement expose une partie des questions de recherche et travaux en cours de notre equipe en geovisualisation (equipe GEOVIS du laboratoire LaSTIG), concernant la visualisation et l'analyse visuelle d'informations spatio-temporelles sur le territoire, via l'interaction et l'immersion. Les questions classiques de la geovisualisation perdurent-quelles representations graphiques, quelles interfaces, quelle qualite. Neanmoins, le contexte a evolue : la complexite des phenomenes et dyna-miques physiques, historiques, sociologiques et leurs interactions avec l'espace geographique, ainsi que le volume de donnees spatiales heterogenes, et les besoins d'utilisateurs tres varies en capacites de vision, perception et cognition, necessitent de faire encore plus converger des domaines connexes sur la representation graphique et l'exploration de donnees, pour ameliorer les capacites d'analyse visuelle en geovisualisation. En particulier, nous presentons ici des questions et travaux de recherche specifiques a l'exploration interactive des capacites de rendu et de representation (carto)graphique de donnees spatio-temporelles dans le contexte de la geovisualisation. Ces travaux s'appliquent a des problematiques liees a la visua-lisation des espaces geographiques urbains et a des problematiques d'analyse de dynamiques urbaines (historique, planification), et de dynamiques geophysiques (inondations, meteorologie). Ces travaux sont implementes sur une plateforme open source de visualisation 3D.

Motivated by the needs of a scalable out-of-core surface reconstruction algorithm available on the cloud, this paper addresses the computation of distributed Delaunay triangulations of massive point sets. The proposed algorithm takes as input a point cloud and first partitions it across multiple processing elements into tiles of relatively homogeneous point sizes. The distributed computation and communication between processing elements is orchestrated so that each one discovers the Delaunay neighbors of its input points within the theoretical overall Delaunay triangulation of all points and computes locally a partial view of this triangulation. This approach prevents memory limitations by never materializing the global triangulation. This efficiency is due to our proposed uncentralized model to represent, manage and locally construct the triangulation corresponding to each tile. The point set is first partitioned into non-overlapping tiles, then we construct within each tile the Delaunay triangulation of the local points and a minimal set of replicated foreign points in order to capture the simplices spanning multiple tiles. Inspired by the star splaying approach for Delaunay triangulation computation/repair, communication is limited to exchanging points of potential Delaunay neighbors across tiles. Therefore, our method is guaranteed to reconstruct, within each tile, a triangulation that contains the star of its local points, as though it were computed within the Delaunay triangulation of all points. The proposed algorithm is implemented with Spark for the scheduling and C++ for the geometric computations. This allows both an optimal scheduling on multiple machines and efficient low-level computation. The results show the efficiency of our algorithm in terms of speedup and strong scaling on a classical Spark configuration with both synthetic and real use case datasets.

We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an end-to-end architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. We first extract high-level 3D features for each point given its 3D neighbors. Then, these features are projected into a 2D multichannel range-image by considering the topology of the sensor. Thanks to these learned features and this projection, we can finally perform the segmentation using a simple U-Net segmentation network, which performs very well while being very efficient. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach outperforms the state-of-the-art by a large margin on the KITTI dataset, as our experiments show. Moreover, this approach operates at 24fps on a single GPU. This is above the acquisition rate of common LiDAR sensors which makes it suitable for real-time applications.