Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds.
References
[1]
Agapaki E., Brilakis I., CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets, Adv. Eng. Inform. 45 (2020),. URL: https://www.sciencedirect.com/science/article/pii/S1474034620300902.
[2]
Agapaki E., Brilakis I.K., Instance segmentation of industrial point cloud data, 2020, CoRR abs/2012.14253, arXiv:2012.14253, URL: https://arxiv.org/abs/2012.14253.
[3]
Agapaki, E., Glyn-Davies, A., Mandoki, S., Brilakis, I., 2019. CLOI: A shape classification benchmark dataset for industrial facilities. In: Computing in Civil Engineering 2019. pp. 66–73. https://doi.org/10.1061/9780784482445.009, arXiv:https://ascelibrary.org/doi/pdf/10.1061/9780784482445.009, URL: https://ascelibrary.org/doi/abs/10.1061/9780784482445.009.
[4]
Bentley J.L., Multidimensional binary search trees used for associative searching, Commun. ACM 18 (9) (1975) 509–517,.
[5]
Birkeland A.A., Streamlining the Creation of Industrial Digital Twins with 3D Semantic and Instance Segmentation, Norwegian University of Science and Technology, 2023, Unpublished master’s thesis.
[6]
Chen S., Fang J., Zhang Q., Liu W., Wang X., Hierarchical aggregation for 3D instance segmentation, 2021, https://doi.org/10.48550/ARXIV.2108.02350, URL: https://arxiv.org/abs/2108.02350.
[7]
Czerniawski, T., Leite, F., 2019. Semantic segmentation of building point clouds using deep learning: A method for creating training data using BIM to point cloud label transfer. In: Computing in Civil Engineering 2019. pp. 410–416. arXiv:https://ascelibrary.org/doi/pdf/10.1061/9780784482421.052, URL: https://ascelibrary.org/doi/abs/10.1061/9780784482421.052, https://doi.org/10.1061/9780784482421.052.
[8]
De Geyter S., Bassier M., Vergauwen M., Automated Training Data Creation for Semantic Segmentation of 3d Point Clouds, in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVI-5/W1-2022, 2022, pp. 59–67,.
[9]
de las Mercedes Gonzalez Stefanelli M.J., Jain A.R., Jalui S.K., Agapaki E., Synthetic point cloud generation for class segmentation applications, in: Proceedings of the 2022 European Conference on Computing in Construction, in: Computing in Construction, vol. 3, University of Turin, Rhodes, Greece, 2022,. URL: https://ec-3.org/publications/conference/paper/?id=EC32022_197.
[10]
Graham B., Engelcke M., van der Maaten L., 3D semantic segmentation with submanifold sparse convolutional networks, 2017, CoRR abs/1711.10275, arXiv:1711.10275, URL: http://arxiv.org/abs/1711.10275.
[11]
Jiang L., Zhao H., Shi S., Liu S., Fu C.-W., Jia J., PointGroup: Dual-set point grouping for 3D instance segmentation, 2020, https://doi.org/10.48550/ARXIV.2004.01658, URL: https://arxiv.org/abs/2004.01658.
[12]
Kaufmann F., Glock C., Tschickardt T., ScaleBIM: Introducing a scalable modular framework to transfer point clouds into semantically rich building information models, in: Proceedings of the 2022 European Conference on Computing in Construction, in: Computing in Construction, vol. 3, University of Turin, Rhodes, Greece, 2022,. URL: https://ec-3.org/publications/conference/paper/?id=EC32022_194.
[13]
Kolodiazhnyi M., Rukhovich D., Vorontsova A., Konushin A., Top-down beats bottom-up in 3D instance segmentation, 2023, arXiv:2302.02871.
[14]
Ma J.W., Czerniawski T., Leite F., Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic bim-based point clouds, Autom. Constr. 113 (2020),. URL: https://www.sciencedirect.com/science/article/pii/S0926580519311884.
[15]
Ma J.W., Han B., Leite F., An automated framework for generating synthetic point clouds from as-built BIM with semantic annotation for scan-to-BIM, in: 2021 Winter Simulation Conference (WSC), 2021, pp. 1–10,.
[16]
Ngo T.D., Hua B.-S., Nguyen K., ISBNet: a 3D point cloud instance segmentation network with instance-aware sampling and box-aware dynamic convolution, 2023, arXiv:2303.00246.
[17]
Noichl F., Braun A., Borrmann A., BIM-to-scan for scan-to-BIM: Generating realistic synthetic ground truth point clouds based on industrial 3D models, in: Proceedings of the 2021 European Conference on Computing in Construction, in: Computing in Construction, vol. 2, ETH, Online Conference, 2021, pp. 164–172,. URL: https://ec-3.org/publications/conference/paper/?id=EC32021_166.
[18]
Park J., Kim J., Lee D., Jeong K., Lee J., Kim H., Hong T., Deep learning–based automation of scan-to-BIM with modeling objects from occluded point clouds, J. Manage. Eng. 38 (4) (2022),. arXiv:https://ascelibrary.org/doi/pdf/10.1061/(ASCE)ME.1943-5479.0001055, URL: https://ascelibrary.org/doi/abs/10.1061/(ASCE)ME.1943-5479.0001055.
[19]
Park J., Kim J., Lee D., Jeong K., Lee J., Kim H., Hong T., Deep learning–based automation of scan-to-BIM with modeling objects from occluded point clouds, J. Manage. Eng. 38 (4) (2022),.
[20]
Qi C.R., Su H., Mo K., Guibas L.J., PointNet: Deep learning on point sets for 3D classification and segmentation, 2017, arXiv:1612.00593.
[21]
Qi C.R., Yi L., Su H., Guibas L.J., PointNet++: Deep hierarchical feature learning on point sets in a metric space, 2017, CoRR abs/1706.02413, arXiv:1706.02413, URL: http://arxiv.org/abs/1706.02413.
[22]
Schult J., Engelmann F., Hermans A., Litany O., Tang S., Leibe B., Mask3D for 3D semantic instance segmentation, 2022, https://doi.org/10.48550/ARXIV.2210.03105, URL: https://arxiv.org/abs/2210.03105.
[23]
Singh M., Srivastava R., Fuenmayor E., Kuts V., Qiao Y., Murray N., Devine D., Applications of digital twin across industries: A review, Appl. Sci. 12 (11) (2022),. URL: https://www.mdpi.com/2076-3417/12/11/5727.
[24]
Sun J., Qing C., Tan J., Xu X., Superpoint transformer for 3D scene instance segmentation, 2022, arXiv:2211.15766.
[25]
VanDerHorn E., Mahadevan S., Digital twin: Generalization, characterization and implementation, Decis. Support Syst. 145 (2021),. URL: https://www.sciencedirect.com/science/article/pii/S0167923621000348.
[26]
Vu T., Kim K., Luu T.M., Nguyen X.T., Yoo C.D., SoftGroup for 3D instance segmentation on point clouds, 2022, https://doi.org/10.48550/ARXIV.2203.01509, URL: https://arxiv.org/abs/2203.01509.
[27]
Yin C., Wang B., Gan V.J., Wang M., Cheng J.C., Automated semantic segmentation of industrial point clouds using ResPointNet++, Autom. Constr. 130 (2021),. URL: https://www.sciencedirect.com/science/article/pii/S0926580521003253.
[28]
Zhang H., Wang T., An efficient method for producing deep learning point cloud datasets based on BIM 3D model and computer simulation, in: Xia J. (Ed.), Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), vol. 12474, SPIE, International Society for Optics and Photonics, 2022,.
[29]
Zhong M., Chen X., Chen X., Zeng G., Wang Y., MaskGroup: Hierarchical point grouping and masking for 3D instance segmentation, in: 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2022,.
Index Terms
- Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds.
Index terms have been assigned to the content through auto-classification.
Recommendations
Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds
Computer Vision – ECCV 2020AbstractWe develop a novel learning scheme named Self-Prediction for 3D instance and semantic segmentation of point clouds. Distinct from most existing methods that focus on designing convolutional operators, our method designs a new learning scheme to ...
Automated registration of multi-view point clouds using sphere targets
Methods of detecting sphere targets using geometric properties are presented.A procedure for fine registration of point-clouds using targets is presented.A new system for point-cloud registration is developed and demonstrated. This paper addresses the ...
Bi-stage multi-modal 3D instance segmentation method for production workshop scene
AbstractThe scene perception of a production workshop is the key to realizing its intelligence. Three-dimensional (3D) instance segmentation based on pure deep learning is an effective method for scene perception, but it is difficult to apply ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier B.V.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 14 March 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Jan 2025