Generating LOD3 building models from structure-from-motion and semantic segmentation Article Swipe
Bryan German Pantoja-Rosero
,
Radhakrishna Achanta
,
Mateusz Koziński
,
Pascal Fua
,
Fernando Pérez‐Cruz
,
Katrin Beyer
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.autcon.2022.104430
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.autcon.2022.104430
This paper describes a pipeline for automatically generating level of detail (LOD) models (digital twins), specifically LOD2 and LOD3, from free-standing buildings. Our approach combines structure from motion (SfM) with deep-learning-based segmentation techniques. Given multiple-view images of a building, we compute a three-dimensional (3D) planar abstraction (LOD2 model) of its point cloud using SfM techniques. To obtain LOD3 models, we use deep learning to perform semantic segmentation of the openings in the two-dimensional (2D) images. Unlike existing approaches, we do not rely on complex input, pre-defined 3D shapes or manual intervention. To demonstrate the robustness of our method, we show that it can generate 3D building shapes from a collection of building images with no further input. For evaluating reconstructions, we also propose two novel metrics. The first is a Euclidean-distance-based correlation of the 3D building model with the point cloud. The second involves re-projecting 3D model facades onto source photos to determine dice scores with respect to the ground-truth masks. Finally, we make the code, the image datasets, SfM outputs, and digital twins reported in this work publicly available in github.com/eesd-epfl/LOD3_buildings and doi.org/10.5281/zenodo.6651663. With this work we aim to contribute research in applications such as construction management, city planning, and mechanical analysis, among others.
Related Topics
Concepts
Point cloud
Computer science
Segmentation
Ground truth
Artificial intelligence
Robustness (evolution)
Structure from motion
Computer vision
Deep learning
Pipeline (software)
Abstraction
Dice
Motion (physics)
Mathematics
Geometry
Chemistry
Biochemistry
Programming language
Epistemology
Philosophy
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.autcon.2022.104430
- OA Status
- hybrid
- Cited By
- 64
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283169615
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283169615Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.autcon.2022.104430Digital Object Identifier
- Title
-
Generating LOD3 building models from structure-from-motion and semantic segmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-20Full publication date if available
- Authors
-
Bryan German Pantoja-Rosero, Radhakrishna Achanta, Mateusz Koziński, Pascal Fua, Fernando Pérez‐Cruz, Katrin BeyerList of authors in order
- Landing page
-
https://doi.org/10.1016/j.autcon.2022.104430Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.autcon.2022.104430Direct OA link when available
- Concepts
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Point cloud, Computer science, Segmentation, Ground truth, Artificial intelligence, Robustness (evolution), Structure from motion, Computer vision, Deep learning, Pipeline (software), Abstraction, Dice, Motion (physics), Mathematics, Geometry, Chemistry, Biochemistry, Programming language, Epistemology, Philosophy, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
64Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 21, 2024: 30, 2023: 10, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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