Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via 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.
Cite
Text
Cazorla et al. "Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/670Markdown
[Cazorla et al. "Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/cazorla2021ijcai-bottleneck/) doi:10.24963/IJCAI.2021/670BibTeX
@inproceedings{cazorla2021ijcai-bottleneck,
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 = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4877-4878},
doi = {10.24963/IJCAI.2021/670},
url = {https://mlanthology.org/ijcai/2021/cazorla2021ijcai-bottleneck/}
}