7.1.1 Cifre PhD Thesis Titane - DS

• Name:
  Software for Cifre PhD thesis Titane - Dassault Systemes
• Keyword:
  Geometric computing
• Functional Description:
  This software addresses the problem of simplifying two-dimensional polygonal partitions that exhibit strong regularities. Such partitions are relevant for reconstructing urban scenes in a concise way. Preserving long linear structures spanning several partition cells motivates a point-line projective duality approach in which points represent line intersections and lines possibly carry multiple points. We implement a simplification algorithm that seeks a balance between the fidelity to the input partition, the enforcement of canonical relationships between lines (orthogonality or parallelism) and a low complexity output. Our methodology alternates continuous optimization by Riemannian gradient descent with combinatorial reduction, resulting in a progressive simplification scheme. Our experiments show that preserving canonical relationships helps gracefully degrade partitions of urban scenes, and yields more concise and regularity-preserving meshes than previous mesh-based simplification approaches.
• Contact:
  Pierre Alliez
• Participants:
  Pierre Alliez, Florent Lafarge
• Partner:
  Dassault Systèmes

7.1.2 CGAL - Orthtree

• Name:
  Quadtrees, Octrees, and Orthtrees
• Keyword:
  Octree/Quadtree
• Functional Description:

  Quadtrees are tree data structures in which each node encloses a square section of space, and each internal node has exactly 4 children. Octrees are a similar data structure in 3D in which each node encloses a cubic section of space, and each internal node has exactly 8 children.

  We call the generalization of such data structure "orthtrees", as orthants are generalizations of quadrants and octants. The term "hyperoctree" can also be found in literature to name such data structures in dimensions 4 and higher.

  This package provides a general data structure Orthtree along with aliases for Quadtree and Octree. These trees can be constructed with custom point ranges and split predicates, and iterated on with various traversal methods.

• Release Contributions:
  Initial version
• Contact:
  Pierre Alliez
• Participants:
  Pierre Alliez, Jackson Campolattaro, Simon Giraudot
• Partner:
  GeometryFactory

7.1.3 CGAL - 3D Alpha Wrapping

• Keyword:
  Shrink-wrapping
• Scientific Description:

  Various tasks in geometric modeling and processing require 3D objects represented as valid surface meshes, where "valid" refers to meshes that are watertight, intersection-free, orientable, and combinatorially 2-manifold. Such representations offer well-defined notions of interior/exterior and geodesic neighborhoods.

  3D data are usually acquired through measurements followed by reconstruction, designed by humans, or generated through imperfect automated processes. As a result, they can exhibit a wide variety of defects including gaps, missing data, self-intersections, degeneracies such as zero-volume structures, and non-manifold features.

  Given the large repertoire of possible defects, many methods and data structures have been proposed to repair specific defects, usually with the goal of guaranteeing specific properties in the repaired 3D model. Reliably repairing all types of defects is notoriously difficult and is often an ill-posed problem as many valid solutions exist for a given 3D model with defects. In addition, the input model can be overly complex with unnecessary geometric details, spurious topological structures, nonessential inner components, or excessively fine discretizations. For applications such as collision avoidance, path planning, or simulation, getting an approximation of the input can be more relevant than repairing it. Approximation herein refers to an approach capable of filtering out inner structures, fine details and cavities, as well as wrapping the input within a user-defined offset margin.

  Given an input 3D geometry, we address the problem of computing a conservative approximation, where conservative means guaranteeing a strictly enclosed input. We seek unconditional robustness in the sense that the output mesh should be valid (oriented, combinatorially 2-manifold and without self-intersections), even for raw input with many defects and degeneracies. The default input is a soup of 3D triangles, but the generic interface leaves the door open to other types of finite 3D primitives.

• Functional Description:
  The algorithm proceeds by shrink-wrapping and refining a 3D Delaunay triangulation loosely bounding the input. Two user-defined parameters, alpha and offset, offer control over the maximum size of cavities where the shrink-wrapping process can enter, and the tightness of the final surface mesh to the input, respectively. Once combined, these parameters provide a means to trade fidelity to the input for complexity of the output
• Contact:
  Pierre Alliez
• Participants:
  Pierre Alliez, Cédric Portaneri, Mael Rouxel-Labbé
• Partner:
  GeometryFactory

7.1.4 CGAL - NURBS meshing

• Name:
  CGAL - Meshing NURBS surfaces via Delaunay refinement
• Keywords:
  Meshing, NURBS
• Scientific Description:
  NURBS is the dominant boundary representation (B-Rep) in CAD systems. The meshing algorithms of NURBS models available for the industrial applications are based on the meshing of individual NURBS surfaces. This process can be hampered by the inevitable trimming defects in NURBS models, leading to non-watertight surface meshes and further failing to generate volumetric meshes. In order to guarantee the generation of valid surface and volumetric meshes of NURBS models even with the presence of trimming defects, NURBS models are meshed via Delaunay refinement, based on the Delaunay oracle implemented in CGAL. In order to achieve the Delaunay refinement, the trimmed regions of a NURBS model are covered with balls. Protection balls are used to cover sharp features. The ball centres are taken as weighted points in the Delaunay refinement, so that sharp features are preserved in the mesh. The ball sizes are determined with local geometric features. Blending balls are used to cover other trimmed regions which do not need to be preserved. Inside blending balls implicit surfaces are generated with Duchon’s interpolating spline with a handful of sampling points. The intersection computation in the Delaunay refinement depends on the region where the intersection is computed. The general line/NURBS surface intersection is computed for intersections away from trimmed regions, and the line/implicit surface intersection is computed for intersections inside blending balls. The resulting mesh is a watertight volumetric mesh, satisfying user-defined size and shape criteria for facets and cells. Sharp features are preserved in the mesh adaptive to local features, and mesh elements cross smooth surface boundaries.
• Functional Description:
  Input: NURBS surface Output: isotropic tetrahedron mesh
• Publication:
  hal-03276860
• Contact:
  Pierre Alliez
• Participants:
  Pierre Alliez, Laurent Busé, Xiao Xiao, Laurent Rineau
• Partner:
  GeometryFactory

8.1.1 Binary Graph Neural Networks

Participants: Gaetan Bahl.

In collaboration with Mehdi Bahri and Stefanos Zafeiriou (Imperial College London).

Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementation challenges of their Euclidean counterparts. Model size, memory footprint, and energy consumption are common concerns for many real world applications. Network binarization allocates a single bit to parameters and activations, thus dramatically reducing the memory requirements (up to 32x compared to single-precision floating-point numbers) and maximizing the benefits of fast SIMD instructions on modern hardware for measurable speedups. However, in spite of the large body of work on binarization for classical CNNs, this area remains largely unexplored in geometric deep learning. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks (See Figure 1). In particular, we present the first dynamic graph neural network in Hamming space, able to leverage efficient k-NN search on binary vectors to speed-up the construction of the dynamic graph. We further verify that the binary models offer significant savings on embedded devices. This work was presented at the Conference on Computer Vision and Pattern Recognition 19.

8.1.2 Planar Shape Based Registration for Multi-modal Geometry

Participants: Muzingzi Li, Florent Lafarge.

We present a global registration algorithm for multi-modal geometric data, typically 3D point clouds and meshes. Existing feature-based methods and recent deep learning based approaches typically rely upon point-to-point matching strategies that often fail to deliver accurate results from defect-laden data. In contrast, we reason at the scale of planar shapes whose detection from input data offers robustness on a range of defects, from noise to outliers through heterogeneous sampling. The detected planar shapes are projected into an accumulation space from which a rotational alignment is operated. A second step then refines the result with a local continuous optimization which also estimates the scale (See Figure 2). We demonstrate the robustness and efficacy of our algorithm on challenging real-world data. In particular, we show that our algorithm competes well against state-of-the-art methods, especially on piece-wise planar objects and scenes. This work was presented at the British Machine Vision Conference 17.

8.1.3 Polygonal Building Segmentation by Frame Field Learning

Participants: Nicolas Girard.

In collaboration with Dmitriy Smirnov and Justin Solomon (MIT) and Yuliya Tarabalka (Luxcarta).

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours (See Figure 3). This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available here. This work was presented at the Conference on Computer Vision and Pattern Recognition 16.

8.1.4 An efficient representation of 3D buildings: application to the evaluation of city models

Participants: Florent Lafarge.

In collaboration with Oussama Ennafii (Gambi-M), Arnaud Le Bris and Clément Mallet (IGN).

City modeling consists in building a semantic generalized model of the surface of urban objects. These could be seen as a special case of Boundary representation surfaces. Most modeling methods focus on 3D buildings with Very High Resolution overhead data (images and/or 3D point clouds). The literature abundantly addresses 3D mesh processing but frequently ignores the analysis of such models. This requires an efficient representation of 3D buildings. In particular, for them to be used in supervised learning tasks, such a representation should be scalable and transferable to various environments as only a few reference training instances would be available. In this paper, we propose two solutions that take into account the specificity of 3D urban models (See Figure 4). They are based on graph kernels and scattering networks. They are here evaluated in the challenging framework of quality evaluation of building models. The latter is formulated as a supervised multilabel classification problem, where error labels are predicted at building level. The experiments show for both feature extraction strategy strong and complementary results (F-score > 74 % for most labels). Transferability of the classification is also examined in order to assess the scalability of the evaluation process yielding very encouraging scores (F-score > 86 % for most labels). This work was presented at the ISPRS congress 20.

8.1.5 Automatic fault mapping in remote optical images and topographic data with deep learning

Participants: Lionel Mattéo, Nicolas Girard, Onur Tasar.

In collaboration with Isabelle Manighetti, Martijn van den Ende, Antoine Mercier, Frederique Leclerc and Tiziano Giampetro (Géoazur), Yuliya Tarabalka (Luxcarta), Jean‐michel Gaucel (Thalès Alenia Space), Stéphane Dominguez and Jacques Malavieille (Université de Montpellier).

Faults form dense, complex multi‐scale networks generally featuring a master fault and myriads of smaller‐scale faults and fractures off its trace, often referred to as damage. Quantification of the architecture of these complex networks is critical to understanding fault and earthquake mechanics. Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time‐consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U‐Net Convolutional Neural Network, to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on a number of performance tests, we select the best performing model, MRef, and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). MRef exhibits good generalization capacities, making it a viable tool for fast and accurate mapping of fracture and fault networks in image and topographic data. The MRef model can thus be used to analyze fault organization, geometry, and statistics at various scales, key information to understand fault and earthquake mechanics (see Figure 5). This work was published in the Journal of Geophysical Research 13.

8.1.6 Geometric approximation of structured scenes from images

Participants: Muzingzi Li.

Geometric approximation of urban objects with compact and accurate representation is a challenging problem that concerns both computer vision and computer graphics communities. Existing literature mainly focuses on reconstruction from high-quality point clouds obtained by laser scanning which are too costly for many practical scenarios. This motivates the investigation into the problem of geometric approximation from low-budget image data. Dense reconstruction from a collection of images is made possible by recent advances in multi-view stereo techniques, yet the resulting point cloud is often far from perfect for generating a compact model. In particular, our goal is to describe the captured scene with a compact and accurate representation. In this thesis, we propose two generic algorithms which address different aspects of image-based geometric approximation. First, we present an algorithm for extracting and vectorizing objects in images with polygons. Second, we present a global registration algorithm from multi-modal geometric data, typically 3D point clouds and surface meshes (Figure 6). Both approaches exploit the detection of linear geometric primitives to approximate either 2D silhouettes with polygons consisting of line segments, or 3D point sets with a collection of planar shapes. The proposed algorithms could be used sequentially to form a pipeline for geometric approximation of an urban object from a set of image data, consisting of an overhead shot for coarse model extraction and multi-view stereo data for generation of point clouds. We demonstrate the robustness and scalability of our methods for structured scenes and objects, as well as applicative potential for free-form objects 22.

8.1.7 Wearable Cooperative SLAM System for Real-time Indoor Localization Under Challenging Conditions

Participants: Pierre Alliez, Fernando Ireta Munoz.

This research has been funded by the ANR/DGA MALIN challenge. In collaboration with the IBSIC lab (Evry) and Innodura TB (SEM from Lyon).

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. We contribute a real-time tracking system for continuously locating emergency response agents in challenging conditions (Figure 7). A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintain the localization. The system achieved an absolute trajectory error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different levels of details 12.

8.2.1 Floorplan generation from 3D point clouds: A space partitioning approach

Participants: Florent Lafarge.

In collaboration with Hao Fang and Hui Huang (Shenzhen University) and Cihui Pan (BeiKe).

We propose a novel approach to automatically reconstruct the floorplan of indoor environments from raw sensor data. In contrast to existing methods that generate floorplans under the form of a planar graph by detecting corner points and connecting them, our framework employs a strategy that decomposes the space into a polygonal partition and selects edges that belong to wall structures by energy minimization. By relying on an efficient space-partitioning data structure instead of a traditional and delicate corner detection task, our framework offers a high robustness to imperfect data. We demonstrate the potential of our algorithm on both RGBD and LIDAR points scanned from simple to complex scenes. Experimental results indicate that our method is competitive with respect to existing methods in terms of geometric accuracy and output simplicity (See Figure 8). This work was published in the ISPRS Journal of Photogrammetry and Remote Sensing 11.

8.2.2 Progressive discrete domains for implicit surface reconstruction

Participants: Tong Zhao, Pierre Alliez.

In collaboration with Laurent Busé (Aromath Inria project-team), Tamy Boubekeur and Jean-Marc Thiery (Télécom ParisTech and Adobe Research, Paris). Tong Zhao is funded by 3IA Côte d'Azur.

Many global implicit surface reconstruction algorithms formulate the problem as a volumetric energy minimization, trading data fitting for geometric regularization. As a result, the output surfaces may be located arbitrarily far away from the input samples. This is amplified when considering i) strong regularization terms, ii) sparsely distributed samples or iii) missing data. This breaks the strong assumption commonly used by popular octree-based and triangulation-based approaches that the output surface should be located near the input samples. As these approaches refine during a pre-process, their cells near the input samples, the implicit solver deals with a domain discretization not fully adapted to the final isosurface. We relax this assumption and propose a progressive coarse-to-fine approach that jointly refines the implicit function and its representation domain, through iterating solver, optimization and refinement steps applied to a 3D Delaunay triangulation. There are several advantages to this approach: the discretized domain is adapted near the isosurface and optimized to improve both the solver conditioning and the quality of the output surface mesh contoured via marching tetrahedra (see Figure 9). This work was presented at the EUROGRAPHICS Symposium on Geometry Processing 15.

8.2.3 Efficient open surface reconstruction from lexicographic optimal chains and critical bases

Participants: Julien Vuillamy.

In collaboration with André Lieutier (Dassault Systemes) and David Cohen-Steiner (Inria Datashape).

Previous works on lexicographic optimal chains have shown that they provide meaningful geometric homology representatives while being easier to compute than their l 1-norm optimal counterparts. This work presents a novel algorithm to efficiently compute lexicographic optimal chains with a given boundary in a triangulation of 3-space, by leveraging Lefschetz duality and an augmented version of the disjoint-set data structure. Furthermore, by observing that lexicographic minimization is a linear operation, we define a canonical basis of lexicographic optimal chains, called critical basis, and show how to compute it (Figure 10). In applications, the presented algorithms offer new promising ways of efficiently reconstructing open surfaces in difficult acquisition scenarios (Figure 11).

8.2.4 Planimetric simplification and lexicographic optimal chains for 3D urban scene reconstruction

Participants: Julien Vuillamy.

This Ph.D. thesis was co-advised by Florent Lafarge, Pierre Alliez and David Cohen-Steiner at Inria, and by André Lieutier from Dassault Systemes.

Creating mesh representations for urban scenes is a requirement for numerous modern applications of urban planning ranging from visualization, inspection, to simulation. Adding to the diversity of possible input data – photography, laser-based acquisitions and existing geographical information system (GIS) data, the variety of urban scenes as well as the large-scale nature of the problem makes for a challenging line of research. Working towards an automatic approach to this problem suggests that a one-fits-all method is hardly realistic. Two independent approaches of reconstruction from point clouds have thus been investigated in this work, with radically different points of view intended to cover a large number of use cases. In the spirit of the GIS community, the first approach makes strong assumptions on the reconstructed scenes and creates a 2.5D piecewise-planar representation of buildings using an intermediate 2D cell decomposition. Constructing these decompositions from noisy or incomplete data often leads to overly complex representations, which lack the simplicity or regularity expected in this context of reconstruction. Loosely inspired by clustering problems such as mean-shift, the focus is put on simplifying such partitions by formulating an optimization process based on a tradeoff between attachment to the original partition and objectives striving to simplify and regularize the arrangement. This method involves working with point-line duality, defining local metrics for line movements and optimizing using Riemannian gradient descent.

The second approach is intended to be used in contexts where the strong assumptions on the representation of the first approach do not hold. We strive here to be as general as possible and investigate the problem of point cloud meshing in the context of noisy or incomplete data. By considering a specific minimization, corresponding to lexicographic orderings on simplicial chains, polynomial-time algorithms finding lexicographic optimal chains, homologous to a given chain or bounded by a given chain, are derived from algorithms for the computation of simplicial persistent homology. For pseudomanifold complexes in codimension 1, leveraging duality and an augmented version of the disjoint-set data structure improves the complexity of these problem instances to quasi-linear time algorithms. By combining its uses with a sharp feature detector in the point cloud, we illustrate different use cases in the context of urban reconstruction 23.

8.3.1 Delaunay Meshing and Repairing of NURBS Models

Participants: Xiao Xiao, Pierre Alliez.

In collaboration with Laurent Busé (Aromath) and Laurent Rineau (Geometry Factory). The postdoctoral stay of Xiao Xiao was funded both by Inria and by Geometry Factory.

CAD models represented by NURBS surface patches are often hampered with defects due to inaccurate representations of trimming curves. Such defects make these models unsuitable to the direct generation of valid volume meshes, and often require trial-and-error processes to fix them. We propose a fully automated Delaunay-based meshing approach which can mesh and repair simultaneously, while being independent of the input NURBS patch layout. Our approach proceeds by Delaunay filtering and refinement, in which trimmed areas are repaired through implicit surfaces. Beyond repair, we demonstrate its capability to smooth out sharp features, defeature small details, and mesh multiple domains in contact (see Figure 13). This work was presented at the EUROGRAPHICS Symposium on Geometry Processing 14. A novel software component for the CGAL library has been implemented.

8.3.2 Alpha wrapping with an offset

Participants: Cedric Portaneri, Pierre Alliez.

In collaboration with Mael Rouxel-Labbe (Geometry Factory), David Cohen-Steiner (Inria DataShape), and Michael Hemmer (Google X).

Various tasks in geometric modeling and processing require 3D objects represented as valid surface meshes, where "valid" refers to meshes that are watertight, intersection-free, orientable, and combinatorially 2-manifold. Such representations offer well-defined notions of interior/exterior and geodesic neighborhoods. 3D data are usually acquired through measurements followed by reconstruction, designed by humans, or generated through imperfect automated processes. As a result, they can exhibit a wide variety of defects including gaps, missing data, self-intersections, degeneracies such as zero-volume structures, and non-manifold features. Given the large repertoire of possible defects, many methods and data structures have been proposed to repair specific defects, usually with the goal of guaranteeing specific properties in the repaired 3D model. Reliably repairing all types of defects is notoriously difficult and is often an ill-posed problem as many valid solutions exist for a given 3D model with defects. In addition, the input model can be overly complex with unnecessary geometric details, spurious topological structures, nonessential inner components, or excessively fine discretizations. For applications such as collision avoidance, path planning, or simulation, getting an approximation of the input can be more relevant than repairing it. Approximation herein refers to an approach capable of filtering out inner structures, fine details and cavities, as well as wrapping the input within a user-defined offset margin. Given an input 3D geometry, we address the problem of computing a conservative approximation, where conservative means guaranteeing a strictly enclosed input (Figure 14). We seek unconditional robustness in the sense that the output mesh should be valid (oriented, combinatorially 2-manifold and without self-intersections), even for raw input with many defects and degeneracies. The default input is a soup of 3D triangles, but the generic design of our method leaves the door open to other types of finite 3D primitives such as segments or points, or even a mixture of those. This work is not published yet.

8.3.3 Geometric Model Reduction Driven by Numerical Simulation Accuracy

Participants: Vincent Vadez, Pierre Alliez.

In collaboration with Francois Brunetti (Dorea technology). This work has been funded by a Cifre collaboration between the Dorea SME and Inria.

When dealing with real-time simulation, radiative thermal computations have always been, and are still a challenge. Notably, computing view factors is very compute-intensive when the input 3D model is complex and exhibits many holes and occlusions. The task is even more difficult on complex geometries generated through topological optimization and on dense meshes required for finite element simulation. This paper focuses on geometric model reduction through mesh decimation. The decimation algorithm is made accurate to the radiative thermal simulation, in order to trade accuracy for computing times. More specifically, the input model is first decomposed into thermal nodes, then we estimate through radiative thermal simulation the sensitivity of decimating each thermal node against a maximum temperature tolerance. Such estimations are then utilized to render the entire mesh decimation process informed by the physical simulation. These estimations are relevant for predicting the amount of decimation applicable to each thermal node, given a user-defined maximum temperature tolerance (see Figure 15). This work was presented at the International Conference on Environmental Systems 18.

X Development LLC (formerly Google X)

Participants: Cédric Portaneri, Pierre Alliez.

We explore an adaptive framework to repair and generate levels of detail from defect-laden 3D measurement data. Repair herein implies to convert raw 3D point sets or triangle soups into watertight solids, which are suitable for computing collision detection and situational awareness of robots. In this context, adaptive stands for the capability to select not only different levels of detail, but also the type of representation best suited to the environment of the scene and the task of the robot. This project focuses on geometry processing for collision detection of robots with mechanical parts and other robots in motion.

Dorea technology

Participants: Vincent Vadez, Pierre Alliez.

In collaboration with SME Dorea Technology, our objective is to advance the knowledge on the radiative thermal simulation of satellites, via geometric model reduction. The survival of a satellite is related to the temperature of its components, the variation of which must be controlled within safety intervals. In this context, the thermal simulation of the satellite for its design is crucial to anticipate the reality of its operation. This CIFRE project started in August 2018, for a total duration of 3 years.

Luxcarta

Participants: Johann Lussange, Florent Lafarge.

The goal of this collaboration is to develop efficient, robust and scalable methods for extracting geometric shapes from the last generation of satellite images (at 0.3m resolution). Besides satellite images, input data will also include classification maps that identify the location of buildings and Digital Surface Models that bring rough pixel-based estimation of the urban object elevation. The geometric shapes will be first restricted to planes that typically describe well the piecewise planar geometry of buildings. The goal will be then to detect and identify each roof section of facade component of a building by a plane in the 3D space. This DGA Rapid project started in September 2020, for a total duration of 2 years.

CSTB

Participants: Mulin Yu, Florent Lafarge.

This collaboration takes the form of an independent contract. The project investigates the design of as-automatic-as-possible algorithms for repairing and converting Building Information Modeling (BIM) models of buildings in different urban-specific CAD formats using combinatorial maps. This project started in November 2019, for a total duration of 3 years.

IRT Saint-Exupéry

Participants: Gaetan Bahl, Florent Lafarge.

This project investigates low-power deep learning architectures for detecting, localizing and characterizing changes in temporal satellite images. These architectures are designed to be exploited on-board satellites with low computational resources. The project started in March 2019, for a total duration of 3 years.

Dassault Systèmes

Participants: Julien Vuillamy, Pierre Alliez, Florent Lafarge.

This project investigates algorithms for reconstructing city models from multi-sourced data. 3D objects are reconstructed by filtering, parsing and assembling planar shapes. The project started in April 2018, for a total duration of 3 years.

10.1.1 FP7 & H2020 projects

We are participating in the BIM2TWIN RIA project (Optimal Construction Management and Production Control), coordinated by CSTB and sponsored by the H2020 “Digital Twins” program. Our overall objective is to build a virtual replica platform, referred to as a digital building twin, to improve construction management, i.e. enhance quality and safety, reduce operational waste, carbon footprints and costs, and shorten schedules. The platform should provide full situational awareness via various sensing modalities in order to monitor human activities during construction and assess conformity to the digital plan. My team will contribute to the conformity assessment part of the platform by providing novel geometry processing pipelines and reliable software components for 3D urban reconstruction.

We are participating in the GRAPES RTN project (learninG, Representing, And oPtimizing shapES), coordinated by ATHENA (Greece). GRAPES aims at advancing the state of the art in Mathematics, CAD, and Machine Learning in order to promote game changing approaches for generating, optimizing, and learning 3D shapes, along with a multisectoral training for young researchers. Concrete applications include simulation and fabrication, hydrodynamics and marine design, manufacturing and 3D printing, retrieval and mining, reconstruction and urban planning. In this context, Pierre Alliez advises a PhD student in collaboration with the beneficiary partner Geometry Factory, an Inria spin-off company commercializing components from the CGAL library. Our objective is to advance the state of the art in applied mathematics, computer-aided design (CAD) and machine learning in order to promote novel approaches for generating, optimizing, and learning 3D shapes. We will focus on dense semantic segmentation of 3D point clouds and semantic-aware reconstruction of 3D scenes. Our goal is to enrich the final reconstructed 3D models with labels that reflect the main semantic class of outdoor scenes such as ground, buildings, roads and vegetation. The Titane project-team will also contribute to the training of young researchers via tutorials on the CGAL library and practical exercises on real-world use cases.

10.2.1 ANR

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

• Pierre Alliez was a reviewer for Computer Graphics Forum.

11.1.4 Invited talks

• Pierre Alliez gave an invited talk at CORESA conference (Compression et représentation des signaux audiovisuels).
• Florent Lafarge gave a talk at the Airbus AI day "Journée d’information Intelligence Artificielle" ("IA et Télédétection : apprendre à extraire des informations géométriques dans les images satellitaires") and at the Inria intro (The challenge of automated geometric modeling of structured objects).

11.1.5 Leadership within the scientific community

Pierre Alliez is a member of the Steering Committees of the EUROGRAPHICS Symposium on Geometry Processing, EUROGRAPHICS Workshop on Graphics and Cultural Heritage and Executive Board Member for the Solid Modeling Association.

11.1.6 Scientific expertise

• Pierre Alliez was a reviewer for the ERC, for the European commission, and is a scientific advisory board member for the Bézout Labex in Paris (Models and algorithms: from the discrete to the continuous).
• Florent Lafarge was a reviewer for the MESR (CIR and JEI expertises).

11.1.7 Research administration

• Pierre Alliez is Head of Science (VP for Science) of the Inria Sophia Antipolis center, in tandem with Fabien Gandon. In this role, he works with the executive team to advise and support the Institute's scientific strategy, to stimulate new research directions and the creation of project-teams, and to manage the scientific evaluation and renewal of the project-teams. This part-time position runs until mid-2022.
• Pierre Alliez is a member of the scientific committee of the 3IA Côte d'Azur.
• Florent Lafarge is a member of the NICE committee. The main actions of the NICE committee are to verify the scientific aspects of the files of postdoctoral students, to give scientific opinions on candidates for national campaigns for postdoctoral stays, delegations, secondments as well as requests for long duration invitations.

11.2.1 Teaching

• Master: Pierre Alliez, advanced machine learning, 10h, M2, Univ. Côte d'Azur, France.
• Master: Pierre Alliez and Gaétan Bahl, deep learning, 21h, M2, Univ. Côte d'Azur, France.
• Master: Pierre Alliez and Florent Lafarge, 3D Meshes and Applications, 32h, M2, Ecole des Ponts ParisTech, France.
• Master: Florent Lafarge, Applied AI, 8h, M2, Univ. Côte d'Azur, France.
• Master: Pierre Alliez and Florent Lafarge, Interpolation numérique, 60h, M1, Univ. Côte d'Azur, France.
• Master: Florent Lafarge and Gaétan Bahl, Mathématiques pour la géométrie, 35h, M1, EFREI, France.

11.2.2 Supervision

• PhD defended in September: Julien Vuillamy, city reconstruction from multi-sourced data, Pierre Alliez and Florent Lafarge.
• PhD defended in September: Muxingzi Li, indoor reconstruction from a smartphone, since February 2018, Florent Lafarge.
• PhD in progress: Gaétan Bahl, low-power neural networks, since March 2019, Florent Lafarge.
• PhD in progress: Mulin Yu, remeshing urban-specific CAD formats, since November 2019, Florent Lafarge.
• PhD in progress: Vincent Vadez, geometric simplification of satellites for thermal simulation, since August 2018, Pierre Alliez.
• PhD in progress: Tong Zhao, shape reconstruction, since November 2019, Pierre Alliez and Laurent Busé (from the Aromath Inria project-team), in collaboration with Jean-Marc Thiery and Tamy Boubekeur (Telecom ParisTech and Adobe research).
• PhD in progress: Rao Fu, since January 2021, Pierre Alliez.
• PhD in progress: Alexandre Zoppis, 3D modeling from satellite images, since October 2021, Florent Lafarge.

11.2.3 Juries

• Pierre Alliez was a PhD thesis reviewer for Pierre-Alain Langlois (Ecole des Ponts ParisTech) and Raphaël Groscot (Paris Dauphine), and a PhD thesis committee member for Yana Nehmé (INSA Lyon).

International journals

• 11 articleH.Hao Fang, F.Florent Lafarge, C.Cihui Pan and H.Hui Huang. Floorplan generation from 3D point clouds: A space partitioning approach.ISPRS Journal of Photogrammetry and Remote Sensing175May 2021, 44-55
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• 12 articleV.Viachaslau Kachurka, B.Bastien Rault, F. I.Fernando Ireta Munoz, D.David Roussel, F.Fabien Bonardi, J.-Y.Jean-Yves Didier, H.Hicham HADJ-ABDELKADER, S.Samia BOUCHAFA, P.Pierre Alliez and M.Maxime Robin. WeCo-SLAM: Wearable Cooperative SLAM System for Real-time Indoor Localization Under Challenging Conditions.IEEE Sensors Journal2021, 1-1
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• 13 articleL.Lionel Mattéo, I.Isabelle Manighetti, Y.Yuliya Tarabalka, J.Jean‐Michel Gaucel, M.Martijn van den Ende, A.Antoine Mercier, O.Onur Tasar, N.Nicolas Girard, F.Frédérique Leclerc, T.Tiziano Giampetro, S.Stéphane Dominguez and J.Jacques Malavieille. Automatic fault mapping in remote optical images and topographic data with deep learning.Journal of Geophysical Research : Solid EarthApril 2021
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• 14 articleX.Xiao Xiao, P.Pierre Alliez, L.Laurent Busé and L.Laurent Rineau. Delaunay Meshing and Repairing of NURBS Models.Computer Graphics Forum4052021, 125-142
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• 15 articleT.Tong ZHAO, P.Pierre Alliez, T.Tamy Boubekeur, L.Laurent Busé and J.-M.Jean-Marc Thiery. Progressive Discrete Domains for Implicit Surface Reconstruction.Computer Graphics Forum4052021, 143-156
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International peer-reviewed conferences

• 16 inproceedingsN.Nicolas Girard, D.Dmitriy Smirnov, J.Justin Solomon and Y.Yuliya Tarabalka. Polygonal Building Segmentation by Frame Field Learning.CVPR 2021 - IEEE Conference on Computer Vision and Pattern RecognitionPittsburg / Virtual, United StatesJune 2021
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• 17 inproceedingsM.Muxingzi Li and F.Florent Lafarge. Planar Shape Based Registration for Multi-modal Geometry.British Machine Vision Conferencevirtual, United Kingdom2021
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• 18 inproceedingsV.Vincent Vadez, F.François Brunetti and P.Pierre Alliez. Geometric Model Reduction Driven by Numerical Simulation Accuracy.ICES 2021 - International Conference on Environmental SystemsLisbon, PortugalJuly 2021
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Conferences without proceedings

• 19 inproceedingsM.Mehdi Bahri, G.Gaétan Bahl and S.Stefanos Zafeiriou. Binary Graph Neural Networks.CVPR 2021 - IEEE Conference on Computer Vision and Pattern RecognitionNashville / Virtual, United StatesJune 2021
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• 20 inproceedingsO.Oussama Ennafii, A.Arnaud Le Bris, F.Florent Lafarge and C.Clément Mallet. An efficient representation of 3D buildings: application to the evaluation of city models.ISPRS 2021 - XXIVth Congress of the International Society for Photogrammetry and Remote SensingXLIII-B2-2021Nice / Virtual, FranceJune 2021, 329 - 336
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• 21 inproceedingsC.Clément Mallet, F.Florent Lafarge, M.Martyna Poreba, T.Teng Wu, G.Gaétan Bahl, M.Mulin Yu, A.Anatol Garioud, Y.Yizi Chen, S.Shenlu Jiang, M. Y.Michael Y Yang and N.Nicolas Paparoditis. Preface: the 2021 edition of the XXIVth ISPRS congress.XXIVth ISPRS Congress, 2021 editionNice (virtuel), FranceJune 2021, 1 - 5
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Doctoral dissertations and habilitation theses

• 22 thesisM.Muxingzi Li. Geometric approximation of structured scenes from images.Université Côte d'AzurOctober 2021
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• 23 thesisJ.Julien Vuillamy. Planimetric simplification and lexicographic optimal chains for 3D urban scene reconstruction.Université Côte d'AzurSeptember 2021
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Reports & preprints

• 24 miscD.David Cohen-Steiner, A.André Lieutier and J.Julien Vuillamy. Efficient open surface reconstruction from lexicographic optimal chains and critical bases.November 2021
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