Knowledge Enhanced Neural Networks for Point Cloud Semantic Segmentation

Author:

Grilli Eleonora1ORCID,Daniele Alessandro2ORCID,Bassier Maarten3ORCID,Remondino Fabio1ORCID,Serafini Luciano2ORCID

Affiliation:

1. 3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, 38121 Trento, Italy

2. Data and Knowledge Management (DKM) Unit, Bruno Kessler Foundation (FBK), 38121 Trento, Italy

3. Department of Civil Engineering, TC Construction-Geomatics, Faculty of Engineering Technology, KU Leuven, 9000 Ghent, Belgium

Abstract

Deep learning approaches have sparked much interest in the AI community during the last decade, becoming state-of-the-art in domains such as pattern recognition, computer vision, and data analysis. However, these methods are highly demanding in terms of training data, which is often a major issue in the geospatial and remote sensing fields. One possible solution to this problem comes from the Neuro-Symbolic Integration field (NeSy), where multiple methods have been defined to incorporate background knowledge into the neural network’s learning pipeline. One such method is KENN (Knowledge Enhanced Neural Networks), which injects logical knowledge into the neural network’s structure through additional final layers. Empirically, KENN showed comparable or better results than other NeSy frameworks in various tasks while being more scalable. Therefore, we propose the usage of KENN for point cloud semantic segmentation tasks, where it has immense potential to resolve issues with small sample sizes and unbalanced classes. While other works enforce the knowledge constraints in post-processing, to the best of our knowledge, no previous methods have injected inject such knowledge into the learning pipeline through the use of a NeSy framework. The experiment results over different datasets demonstrate that the introduction of knowledge rules enhances the performance of the original network and achieves state-of-the-art levels of accuracy, even with subideal training data.

Funder

project “AI@TN” funded by the Autonomous Province of Trento, Italy, the FWO Postdoc grant

Geomatics research group of the Department of Civil Engineering, TC Construction at the KU Leuven in Belgium

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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