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Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes

arXiv 2202.04834 ↗First posted 10 Feb 2022cs.CVcited by 0

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Abstract (arXiv)

This paper devises, implements and benchmarks a novel shape retrieval method that can accurately match individual labelled point clusters (instances) of existing industrial facilities with their respective CAD models. It employs a combination of image and point cloud deep learning networks to classify and match instances to their geometrically similar CAD model. It extends our previous research on geometric digital twin generation from point cloud data, which currently is a tedious, manual process. Experiments with our joint network reveal that it can reliably retrieve CAD models at 85.2% accuracy. The proposed research is a fundamental framework to enable the geometric Digital Twin (gDT) pipeline and incorporate the real geometric configuration into the Digital Twin.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 129 references that are in the map. First posted on arXiv 10 Feb 2022.

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19 of 129 references.

References · 19

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  12. 2017SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentationcited by 168
7 more
  1. 2021Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networkscited by 98
  2. 2018Supervised Fitting of Geometric Primitives to 3D Point Cloudscited by 90
  3. 2020A Closer Look at Local Aggregation Operators in Point Cloud Analysiscited by 87
  4. 2019Associatively Segmenting Instances and Semantics in Point Cloudscited by 86
  5. 2021Learning with Noisy Labels for Robust Point Cloud Segmentationcited by 22
  6. 2018Improving Image Clustering With Multiple Pretrained CNN Feature Extractorscited by 12
  7. 2020Instance Segmentation of Industrial Point Cloud Datacited by 5

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