# Classifying point clouds at the facade-level using geometric features and deep learning networks

arXiv: https://arxiv.org/abs/2402.06506 · HTML: https://mlsanity.com/paper/2402.06506/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [A review on deep learning techniques for 3D sensed data classification](https://mlsanity.com/paper/1907.04444/) (2019)
- [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, 3 of 3)

- [Texture2LoD3: Enabling LoD3 Building Reconstruction With Panoramic Images](https://mlsanity.com/paper/2504.05249/) (2025)
- [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)

## Abstract (arXiv)

3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods' performance. This method can be applied for compensating deep learning networks' ability in capturing local geometric information and promoting the advancement of semantic segmentation.