# Reconstructing facade details using MLS point clouds and Bag-of-Words approach

arXiv: https://arxiv.org/abs/2402.06521 · HTML: https://mlsanity.com/paper/2402.06521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds](https://mlsanity.com/paper/1904.00229/) (2019)
- [Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification](https://mlsanity.com/paper/2303.05998/) (2023)

## Abstract (arXiv)

In the reconstruction of faccade elements, the identification of specific object types remains challenging and is often circumvented by rectangularity assumptions or the use of bounding boxes. We propose a new approach for the reconstruction of 3D faccade details. We combine MLS point clouds and a pre-defined 3D model library using a BoW concept, which we augment by incorporating semi-global features. We conduct experiments on the models superimposed with random noise and on the TUM-FAcCADE dataset. Our method demonstrates promising results, improving the conventional BoW approach. It holds the potential to be utilized for more realistic facade reconstruction without rectangularity assumptions, which can be used in applications such as testing automated driving functions or estimating faccade solar potential.