Automated production of synthetic point clouds of truss bridges for semantic and instance segmentation using deep learning models

Lamas, Daniel; Justo, Andrés; Soilán, Mario; Riveiro, Belén

doi:10.1016/j.autcon.2023.105176

Cited by 4 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BridgeNetv2 is then tested on seven real bridge point clouds. A similar strategy regarding the use of synthetic data for training has been adopted in other recent studies (Korus et al, 2023;Lamas et al, 2024), due to the lack of suitable open-source point cloud data for conducting segmentation training of civil engineering assets. Due to this data scarcity issue, BridgeNetv2 is only applied to the masonry arch bridge dataset available to the authors.…”

Section: Introductionmentioning

confidence: 99%

A lightweight Transformer‐based neural network for large‐scale masonry arch bridge point cloud segmentation

Jing,

Sheil,

Acikgoz

2024

Computer aided Civil Eng

View full text Add to dashboard Cite

Transformer architecture based on the attention mechanism achieves impressive results in natural language processing (NLP) tasks. This paper transfers the successful experience to a 3D point cloud segmentation task. Inspired by newly proposed 3D Transformer neural networks, this paper introduces a new Transformer‐based module, which is called Local Geo‐Transformer. To alleviate the heavy memory consumption of the original Transformer, Local Geo‐Transformer only performs the attention mechanism in local regions. It is designed to mitigate the information loss caused by the subsampling of point clouds for segmentation. Global Geo‐Transformer is proposed to exploit the global relationships between features with the lowest resolution. The new architecture is validated on a masonry bridge dataset developed by the authors for their earlier work on a previous segmentation network called BridgeNet. The new version of the network with Transformer architecture, BridgeNetv2, outperforms BridgeNet in all metrics. BridgeNetv2 is also shown to be lightweight and memory efficient, well‐adapted to large‐scale point cloud segmentation tasks in civil engineering.

show abstract

Section: Introductionmentioning

confidence: 99%