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NTIS 바로가기Journal of KIBIM = 한국BIM학회논문집, v.13 no.4, 2023년, pp.96 - 105
김태훈 (경북대학교 건축학과) , 구형모 ((주)위니텍) , 홍순민 (경북대학교 건축학과) , 추승연 (경북대학교 건축학부)
This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introd...
Kang, K., Wang, X. (2014). Fully Convolutional Neural Networks for Crowd Segmentation, arXiv:1411.4464, https://?arxiv.org/abs/1411.4464 (Nov. 18. 2023).
Kwon, S. C., Kim, M. Y., Kim, T. Y. (2008). The Choice of?Measurement Techniques in Survey and Recording of?Historic Building in Mordern Ages of Korea, Autumn?Annual Conference of AIK 2008, 28(1), pp. 431-434.
Kwon, W., Chun, J. Y. (2006). Constitution of Work Process?for the Remodeling Construction Project in Planning?Phase, Korean Journal of Construction Engineering and?Management, 7(6), pp. 165-174.
Lim, H., Lee, Y., Jee, M., Go, M., Kim, H., Kim, Wo. (2019).?Efficient Inference of Image Objects using Semantic?Segmentation, Journal of Broadcast Engineering (JBE),?24(1), pp. 67-76.
Long, J., Shelhamer, E., Darrell, T. (2015). Fully Convolutional?Networks for Semantic Segmentation, CVPR 2015, pp7).?3431-3440.
Long, J., Shelhamer, E., Darrell, T. (2015). Fully Convolutional?networks for Semantic Segmentation, Proceedings of the?IEEE conference on computer vision and pattern?recognition, pp. 3431-3440.
Min, J. S., Park, H. K, (2001). A Study on the Remodeling?Process of Building, Autumn Annual Conference of AIK?2001, 21(2), pp. 127-130.
Noh, H., Hong, S., Han, B. (2015) Learning Deconvolution?Network for Semantic Segmentation, arXiv:1505.04366,?https://arxiv.org/abs/1505.04366 (No. 22. 2023).
Noh, H., Hong, S., Han, B. (2015) Learning Deconvolution?Network for Semantic Segmentation, arXiv:1505.04366,?https://arxiv.org/abs/1505.04366 (Nov. 22. 2023).
Qi, C. R., Su, H., Mo, K., Guibas, L. J. (2017a). PointNet: Deep?Learning on Point Sets for 3D Classification and Segmentation, arXiv:1612.00593v2, https://arxiv.org/abs/1612.00593v2 (Dec. 20. 2023).
Qi, C. R., Su, H., Mo, K., Guibas, L. J. (2017a). Pointnet: Deep?Learning on Point Sets for 3D Classification and?Segmentation, Proceedings of the IEEE Conference on?Computer Vision and Pattern Recognition, pp. 652-660.
Qi, C. R., Yi, L., Su, H., Guibas, L. J, (2017b). PointNet++:?Deep Hierarchical Feature Learning on Point Sets in a?Metric Space, Advances in Neural Information Processing?Systems, 30.
Rukhovich, D., Vorontsova, A., Konushin, A. (2021), FCAF3D:?Fully Convolutional Anchor-Free 3D Object Detection,?European Conference on Computer Vision, pp. 477-493.
Yang, B., Luo, W., Urtasun, R. (2018). PIXOR: Real-time 3D?Object Detection from Point Clouds, Proceedings of the?IEEE conference on Computer Vision and Pattern Recognition, pp. 7652-7660.
Zhou, Y., Tuzel, O. (2018). VoxelNet: End-to-End Learning?for Point Cloud Based 3D Object Detection, Proceedings?of the IEEE conference on computer vision and pattern?recognition, pp.4490-4499.
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