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IC classifier: a classifier for 3D industrial components based on geometric prior using GNN

arXiv 2303.05730 ↗First posted 10 Mar 2023cs.CVcited by 0

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Abstract (arXiv)

In this paper, we propose an approach to address the problem of classifying 3D industrial components by introducing a novel framework named IC-classifier (Industrial Component classifier). Our framework is designed to focus on the object's local and global structures, emphasizing the former by incorporating specific local features for embedding the model. By utilizing graphical neural networks and embedding derived from geometric properties, IC-classifier facilitates the exploration of the local structures of the object while using geometric attention for the analysis of global structures. Furthermore, the framework uses point clouds to circumvent the heavy computation workload. The proposed framework's performance is benchmarked against state-of-the-art models, demonstrating its potential to compete in the field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 10 Mar 2023.

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11 of 24 references.

References · 11

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  4. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  5. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 310
  6. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  7. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  8. 2019Unsupervised Multi-Task Feature Learning on Point Cloudscited by 89
  9. 2020TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domaincited by 65
  10. 2018Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networkscited by 25
  11. 2014Deep Learning Representation using Autoencoder for 3D Shape Retrievalcited by 6

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