| Titre : |
Geometric and semantic joint approach for the reconstruction of digital models of buildings |
| Type de document : |
Thèse/HDR |
| Auteurs : |
Pierre-Alain Langlois, Auteur ; Renaud Marlet, Directeur de thèse ; Alexandre Boulch, Directeur de thèse |
| Editeur : |
Champs-sur-Marne : Ecole des Ponts ParisTech |
| Année de publication : |
2021 |
| Importance : |
131 p. |
| Format : |
21 x 30 cm |
| Note générale : |
Bibliographie
Thèse de doctorat de l’Ecole des Ponts ParisTech, Spécialité Informatique |
| Langues : |
Anglais (eng) |
| Descripteur : |
[Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] jeu de données localisées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconnaissance de surface
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] texture d'image
|
| Index. décimale : |
THESE Thèses et HDR |
| Résumé : |
(Auteur) The advent of Building Information Models (BIM) in the field of construction and city management revolutionizes the way we design, build, operate and maintain our buildings. BIM models not only include the geometric aspect of the buildings but also semantic information which identifies its logical components (walls, slabs, windows, doors, etc..). While this information is fairly reasonable to create during the building design, only 1% of the building stock is renewed each year. There is therefore an increasing need for automated methods to generate BIM models on existing buildings from sensors such as simple RGB cameras or more advanced Lidar sensors which directly provide a point cloud.In this context, the goal of this thesis is to develop approaches for BIM reconstruction, including both geometric reconstruction and semantic analysis.These tasks have been explored, but an important research effort is conducted to make them more robust to the variety of use cases found in practice.3D reconstruction is usually operated based on direct 3D acquisitions such as Lidars or using photogrammetry, i.e., using pictures to triangulate key point locations and reconstruct the surface afterward. In the context of buildings, the later case is usually limited by the presence of textureless areas which make it hard for the algorithms to find key points and to triangulate them. Moreover, some parts of the buildings might be missing from the input data because of occlusions or omission from the acquisition operator.Regarding semantics in point clouds, important ambiguities exist between semantic classes: the discontinuity between a wall and a door can be hard to distinguish; a slab, a roof and a ceiling sometimes need additional context to be disentangled.In this thesis, we present three technical contributions to address these issues.First, for 3D reconstruction of building scenes, we propose the first method to reconstruct piecewise-planar scenes from images using line segments as primitives. While wide textureless areas exist in indoor scenes (e.g., walls), making it particularly difficult to detect key points, lines are often more visible and easier to detect (e.g., change of illumination at the intersection of two walls) and therefore should be used to ensure robustness of image-based reconstruction approaches. We leverage the presence of these line segments and the possibility to detect and triangulate them. This makes the method robust to textureless surfaces, and we show that we can reconstruct scenes on which point-based methods fail.The second contribution is more theoretical and addresses the problem of mesh reconstruction from multiple calibrated images of low resolution. In this context, traditional methods completely fail and directly learning priors on a large scale dataset of 3D shapes allows us to still perform reconstruction. More specifically, our method uses the learned priors to provide an initial rough shape which is further refined by incorporating geometric constraints. Our method directly outputs a mesh and competes with state of the art methods which can only output a noisy point cloud.Finally, the third technical contribution is VASAD, a dataset for volumetric and semantic reconstruction, which we created from raw BIM models available online. It is the first large scale dataset (62000m²) to offer both geometric and semantic annotation at point and mesh level. With this dataset, we propose two methods to jointly reconstruct both geometry and semantics from a point cloud and we show that the proposed dataset is challenging enough to stimulate research. |
| Note de contenu : |
1. Introduction
1.1 Motivation
1.2 Approach
1.3 Contributions
1.4 Organization of the dissertation
SURFACE RECONSTRUCTION FROM 3D LINE SEGMENTS
2. Introduction
2.1 Reconstructing textureless surfaces
2.2 Related Work
3. Method
3.1 Line extraction
3.2 Plane detection from 3D line segments
3.3 Surface reconstruction
4. Results
4.1 Datasets
4.2 Observations on the input data
4.3 Qualitative evaluation of reconstructions
4.4 Quantitative evaluation of reconstructions
4.5 Ablation study
4.6 Limitations and perspectives
4.7 Conclusion
3D RECONSTRUCTION BY PARAMETERIZED SURFACE MAPPING
5. Introduction
5.1 Learning 3D reconstruction
5.2 Related work
6. Method
6.1 Learning a Multi-View Parameterized Surface Mapping
6.2 Design choices
7. Results
7.1 Dataset
7.2 Evaluation criteria
7.3 Experimental results
7.4 Ablation study
7.5 Discussion and limitations
7.6 Conclusion
VASAD: A VOLUME AND SEMANTIC DATASET FOR BUILDING RECONSTRUCTION FROM POINT CLOUDS
8. Introduction
8.1 3D Reconstruction for buildings
8.2 Related work
9. DATASET
9.1 Building information models
9.2 Presentation of the dataset
9.3 3D representation
9.4 Point cloud simulation
9.5 Train/test split
10. Method
10.1 Reconstruction approaches
10.2 PVSRNet
10.3 Semantic Convolutional Occupancy Network
10.4 Data preparation
11. RESULTS
11.1 Metrics
11.2 Surface reconstruction
11.3 Semantic segmentation
11.4 Discussion
11.5 Conclusion
EPILOGUE
12. Conclusion
12.1 Looking back
12.2 Looking ahead
|
| Numéro de notice : |
26822 |
| Affiliation des auteurs : |
non IGN |
| Thématique : |
IMAGERIE/URBANISME |
| Nature : |
Thèse française |
| Note de thèse : |
Thèse de Doctorat : informatique : Champs-Sur-Marne : 2021 |
| Organisme de stage : |
Laboratoire d'Informatique Gaspard Monge LIGM |
| nature-HAL : |
Thèse |
| DOI : |
sans |
| Date de publication en ligne : |
11/04/2022 |
| En ligne : |
https://tel.hal.science/tel-03637158/ |
| Format de la ressource électronique : |
URL |
| Permalink : |
https://documentation.ensg.eu/index.php?lvl=notice_display&id=100564 |
|  |
Geometric and semantic joint approach for the reconstruction of digital models of buildings (2021)
