# MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models

arXiv: https://arxiv.org/abs/2402.06288 · HTML: https://mlsanity.com/paper/2402.06288/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks](https://mlsanity.com/paper/2305.06314/) (2023)
- [Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification](https://mlsanity.com/paper/2303.05998/) (2023)

## Built on by (most cited first, 4 of 4)

- [TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset](https://mlsanity.com/paper/2505.07396/) (2025)
- [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)
- [FacaDiffy: Inpainting Unseen Facade Parts Using Diffusion Models](https://mlsanity.com/paper/2502.14940/) (2025)
- [GeoTexBuild: 3D Building Model Generation from Map Footprints](https://mlsanity.com/paper/2504.08419/) (2025)

## Abstract (arXiv)

Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper. Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals. We believe that our method can foster development of LoD3 reconstruction algorithms and subsequently enable their wider adoption.