Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning

Cazorla, Romain; Poinel, Line; Papadakis, Panagiotis; Buche, Cédric

doi:10.24963/ijcai.2021/670

Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning

Romain Cazorla, Line Poinel, Panagiotis Papadakis, Cédric Buche

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4877-4878.

https://doi.org/10.24963/ijcai.2021/670

Keywords

Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
Machine Learning Applications: Applications of Supervised Learning
Computer Vision: 2D and 3D Computer Vision
Machine Learning: Deep Learning

Abstract

Point cloud acquisition techniques are an essential tool for the digitization of industrial plants, yet the bulk of a designer's work remains manual. A first step to automatize drawing generation is to extract the semantics of the point cloud. Towards this goal, we investigate the use of deep learning to semantically segment oil and gas industrial scenes. We focus on domain characteristics such as high variation of object size, increased concavity and lack of annotated data, which hampers the use of conventional approaches. To address these issues, we advocate the use of synthetic data, adaptive downsampling and context sharing.

Romain Cazorla, Line Poinel, Panagiotis Papadakis, and Cédric Buche. 2021. Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4877–4878. https://doi.org/10.24963/ijcai.2021/670

@inproceedings{ijcai2021p670,
  title     = {Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning},
  author    = {Cazorla, Romain and Poinel, Line and Papadakis, Panagiotis and Buche, Cédric},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4877--4878},
  year      = {2021},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2021/670},
  url       = {https://doi.org/10.24963/ijcai.2021/670},
}

TY  - CPAPER
TI  - Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning
AU  - Cazorla, Romain
AU  - Poinel, Line
AU  - Papadakis, Panagiotis
AU  - Buche, Cédric
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4877
EP  - 4878
DO  - 10.24963/ijcai.2021/670
UR  - https://doi.org/10.24963/ijcai.2021/670
ER  -