Update-Friendly Data Structures For 3D City Modelling H/F - 06

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.
Update-friendly data structures for 3D city modelling
Le descriptif de l'offre ci-dessous est en Anglais
Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Chercheur contractuel

A propos du centre ou de la direction fonctionnelle

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

Principales activités

Context

In a world facing profound upheavals-ecological, energy-related, economic, health-related, social, and more-territories are at the heart of the most complex decisions. The stakeholders within these territories are under increasing pressure to anticipate, adapt, and invent new approaches to planning and development. It is now essential to be able to anticipate territorial evolution and simulate different management scenarios in order to assess, and even compare, their impacts. This is the objective of the Digital Twin of France and its Territories (JNFT) project, initiated and co-led by the IGN (National Institute of Geographic and Forest Information), Cerema (Center for Studies and Expertise on Risks, Environment, Mobility and Urban Planning), and Inria (National Institute for Research in Digital Science and Technology).

The 3D reconstruction of urban objects - in particular the buildings - from remote sensing data - typically Airborne Lidar Scans (ALS) - constitutes one of the core research axes of the JNFT project. Urban reconstruction from Lidar point cloud is a multifaceted problem in which output models are expected to be (i) accurate (adherence to the input data) and (ii) of high geometric quality (conciseness, conforming to urban-specific formalisms and geometric guarantees), and algorithms that produce these models must be (iii) automatic, (iv) fast and scalable, (v) generic to adapt to the variety of urban landscapes, and (vi) geometrically memory-efficient for easy updates of the reconstructed objects along time. The existing methods [1,2,3] offer efficient solutions to criteria (i-iv), but tend to overlook criteria (v) and (vi). In particular, these methods rely upon the construction of space partitioning data structures in 2D and/or 3D that take the form of line and plane arrangements [4,5,6,7]. These data structures allow a limited piecewise-planar description of buildings only, without the capacity of finely approximating non planar components or approaching them by more complex parametric shapes. Once constructed, these data structures can also not be easily modified.

Objectives

The goal of this position is to investigate a new generation of space partitioning data structures that address the limitations mentioned previously. The candidate will design them in the perspective of replacing the traditional data structures used in the JNFT project, working in collaboration with Luxcarta and GeometryFactory partners and using the CGAL library [8]. The candidate will work on two complementary research directions.

The candidate will investigate how to generalize line and plane arrangements to more expressive data structures that can better capture the geometry of non-planar shapes, e.g. curved facades and freeform roofs. The hybridization of line and plane arrangements with fine mesh-based structures, as proposed in [9], or the use of parametric shapes with a CSG-based construction tree are two options to explore. This generalization will have to be efficient. In particular, it should not degrade the performance in terms of construction time and scalability of the current line and plane arrangements.

The candidate will also investigate the design of update-friendly data structures that can be easily modified once constructed. This characteristic is crucial for efficiently updating the 3D city models, i.e. by only locally modifying the geometry of objects where changes have been detected without reconstructing the entire objects and scenes. Binary Space Partitioning trees, as used in [5,6], could be a solution to this challenge if coupled with an efficient parsing strategy of the geometric atomic elements forming the data structures (e.g. planes and lines).

Keywords

Geometry processing, Computational geometry, 3D computer vision, geometric data structures, urban reconstruction

References

[1] Bauchet, Sulzer, Lafarge and Tarabalka. SimpliCity: Reconstructing Buildings with Simple Regularized 3D Models. CVPRW, 2024

[2] Peters, Dukai, Vitalis, Van Liempt and Stoter. Automated 3D Reconstruction of LoD2 and LoD1 Models for All 10 million Buildings of the Netherlands. PE&RS journal 2022.

[3] Huang, Stoter, Peters, and Nan. City3d: Large-scale building reconstruction from airborne lidar point clouds. Remote Sensing, 2022.

[4] Bauchet and Lafarge. Kinetic Shape Reconstruction. Trans. On Graphics, 2020.

[5] Sulzer and Lafarge. Concise Plane Arrangements for Low-Poly Surface and Volume Modelling. ECCV 2024

[6] Pan, Zhang, Liu, Gong and Huang. Building LOD Representation for 3D Urban Scenes. P&RS journal, 2025

[7] Wu et al. MorphCut: An Efficient Convex Decomposition Method of 3D Building Models for Urban Morphological Analytics. International Journal of Geographical Information Science, 2025

[8] The CGAL Project. CGAL User and Reference Manual. CGAL Editorial Board, 5.5.1 edition, 2022.

[9] Jiang et al. Structure-Aware Surface Reconstruction via Primitive Assembly. ICCV 2023.

More info can be found at

Compétences

The ideal candidate should have a Ph.D. in computer science with a strong background in 3D geometry (geometry processing and/or computational geometry), Computer Vision and applied mathematics, be able to program in C/C++, be fluent in English, and be creative and rigorous.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage