2023
DOI: 10.3390/rs15102590
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Knowledge Enhanced Neural Networks for Point Cloud Semantic Segmentation

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 i… Show more

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Cited by 12 publications
(4 citation statements)
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“…Further, Grilli et al. (2023) integrated knowledge‐enhanced neural networks (KENN) with the transformer network to enhance the segmentation capability with additional logical reasoning. These end‐to‐end point‐based models typically utilize point clouds directly, focusing primarily on extracting representative features of objects in a multidimensional space.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Further, Grilli et al. (2023) integrated knowledge‐enhanced neural networks (KENN) with the transformer network to enhance the segmentation capability with additional logical reasoning. These end‐to‐end point‐based models typically utilize point clouds directly, focusing primarily on extracting representative features of objects in a multidimensional space.…”
Section: Literature Reviewmentioning
confidence: 99%
“…For example, identifying different types of columns in a heavily eroded setting can greatly benefit from both visual and geometric interpretations, even when the latter might introduce noise. Multi-modal data fusion in machine learning is a growing sector (Townend et al, 2024) and some recent works started also to introduce background knowledge into the neural network's learning pipelines (Grilli et al, 2023).…”
Section: Introductionmentioning
confidence: 99%
“…Operative approaches for point cloud categorization rely on hand-crafted feature extraction rules and a variety of machine learning-based classifiers (Zhang et al, 2023). With the advancements in deep learning-based techniques, the use of deep neural networks has gained traction (Hu et al, 2020;Hu et al, 2021;Mao et al, 2022;Ren et al, 2023), including the combination of logic rules (Grilli et al, 2023). Although there have been positive findings, the classification of 3D point clouds still encounters numerous difficulties when applied in real-world scenarios.…”
Section: Introductionmentioning
confidence: 99%