Cost-Efficient Image Semantic Segmentation for Indoor Scene Understanding Using Weakly Supervised Learning and BIM-Reference-Cited by-同舟云学术

Cost-Efficient Image Semantic Segmentation for Indoor Scene Understanding Using Weakly Supervised Learning and BIM

Published:2023-03 Issue:2 Volume:37 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Yang Liu¹,Cai Hubo²^ORCID

Affiliation:

1. Ph.D. Student, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

2. Professor, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5065

Reference101 articles.

1. BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images

2. Acharya D. K. Khoshelham and S. Winter. 2019b. “Unimelb corridor synthetic dataset.” Accessed April 24 2022. https://melbourne.figshare.com/articles/dataset/UnimelbCorridorSynthetic_zip/10930457.

3. Single-image localisation using 3D models: Combining hierarchical edge maps and semantic segmentation for domain adaptation

4. Ahn J. and S. Kwak. 2018. “Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation.” In Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition 4981–4990. New York: IEEE.

5. Alawadlhi M. and W. Yan. 2020. “BIM hyperreality—Data synthesis using BIM and hyperrealistic rendering for deep learning.” In Proc. 40th Annual Conf. of the Association of Computer Aided Design in Architecture (ACADIA) 2020 228–236. Fargo ND: Association of Computer Aided Design in Architecture.

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