# Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification

arXiv: https://arxiv.org/abs/2303.05998 · HTML: https://mlsanity.com/paper/2303.05998/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)

## Built on by (most cited first, 8 of 8)

- [Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks](https://mlsanity.com/paper/2305.06314/) (2023)
- [TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset](https://mlsanity.com/paper/2505.07396/) (2025)
- [MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models](https://mlsanity.com/paper/2402.06288/) (2024)
- [ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset](https://mlsanity.com/paper/2411.04865/) (2024)
- [Classifying point clouds at the facade-level using geometric features and deep learning networks](https://mlsanity.com/paper/2402.06506/) (2024)
- [CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps](https://mlsanity.com/paper/2508.15672/) (2025)
- [FacaDiffy: Inpainting Unseen Facade Parts Using Diffusion Models](https://mlsanity.com/paper/2502.14940/) (2025)
- [Reconstructing facade details using MLS point clouds and Bag-of-Words approach](https://mlsanity.com/paper/2402.06521/) (2024)

## Abstract (arXiv)

Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no faccade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models' faccades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate faccade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points' classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAcCADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with faccade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms.