CLOI: A Shape Classification Benchmark Dataset for Industrial Facilities
COMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCE
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Agapaki, E., Glyn-Davies, A., Mandoki, S., & Brilakis, I. (2019). CLOI: A Shape Classification Benchmark Dataset for Industrial Facilities. COMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCE, 66-73. https://doi.org/10.17863/CAM.36600
Generation of digital models of existing industrial facilities is labor intensive and expensive. The use of state-of-the-art deep learning algorithms can assist to reduce the modelling time and cost. However large databases of labelled, laser-scanned industrial facilities do not exist to date, henceforth training of deep learning models is not possible. Our paper solves this problem by proposing a new benchmark dataset, which consists of five labelled industrial plants. The labelling schema that we followed for the generation of this dataset is based on the frequency of appearance of industrial object types. We labelled the ten most frequent industrial object shapes as identified in previous work. We present CLOI (Channels, L-shapes, circular sections, I-shapes): a richly annotated large-scale repository of shapes represented by labelled point clusters. CLOI has more than 140 million hand labelled points and serves as the foundation for researchers who are interested in automated modelling of industrial assets using deep learning algorithms.
Leverhulme Trust (IAF-2018-011)
This record's DOI: https://doi.org/10.17863/CAM.36600
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289351