Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Nagatomo, H. (2003) Draw Your Own Manga: All the Basics , Kodansha International, Japan

	Nagumo, H. (1999) Colour image chart , Graphic-sha, Japan

	Nagumo, H. (1999) Colour image chart , Graphic-sha, Japan

	Nagumo, H. (2000) Color Image Chart , Long See International Book Co Ltd., Taipei. (in Chinese)

	Naito, H. (2013) Hiroshi Naito : from protoform to protoscape: Tokyo-to Minato-ku , TOTO Shuppan

	Naja, H. (1999) Multiview databases for building modelling , Automation in Construction, 8: 567-579

	Naja, H. (1999) Multiview databases for building modelling , Automation in Construction No. 5 June 1999 pp. 567-579

	Nakamura, H., Morozumi, M. and Kijima, Y. (1991) CG system to support excavation work on the site and analytical study off the site , The forth international conference on computing in civil and building engineering extend abstracts July, Tokyo, 1991 320

	Nakamura, H., Morozumi, M., Iki, K. and Nakajo, K. (1995) Development of 3D CG system to support excavation work. Research to Space composition of remains , Proceedings of the eighteen symposium on computer technology of information, systems and applications December, Tokyo, 1995 19–24

	Nakashima, H (2007) Cyber assist project for ambient intelligence , JC Augusto and D Shapiro (eds), Advances in Ambient Intelligence, IOS Press, pp. 1-20

	NALL, D. H. and CRAWLEY, D. B. (1983) Energy simulation in the building design process , ASHRAE Journal, 25 (11): 28-32, November

	Nam,C. H., Tatum,C. B. (1992) Noncontractual Methods of Integration on ConstructionProjects , Journal of Construction Engineering and Management, 118, 385-398

	Nand, K., Baghaei, N., Casey, J., Barmada, B., Mehdipour, F., & Liang, H.-N. (2019) Engaging children with educational content via Gamification , Smart Learning Environments, 6(1), 6. https://doi.org/10.1186/s40561-019-0085-2

	Nanda, U, Pati, D, Ghamari, H and Bajema, R (2013) Lessons from neuroscience: Form follows function; emotions follow form , Intelligent Buildings International, 5, p 61-78

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing. https://doi.org/10.1007/978-3-030-58452-8_10

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation , arXiv preprint arXiv:2003.06988

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , European Conference on Computer Vision (ECCV). pp. 162-177. Available at: https://doi.org/10.1007/978-3-030-58452-8

	Nauata, N., Hosseini, S., Chang, K. H., Chu, H., Cheng, C. Y., & Furukawa, Y. (2021) House-GAN++: Generative adversarial layout refinement network towards intelligent computational agent for professional architects , 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Nashville, TN, USA (IEEE). 2021: 13627-13636. In CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 13627-13636). https://doi.org/10.1109/CVPR46437.2021.01342

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