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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	Narayanan V, Wu K, Yuksel C, et al. (2019) Visual knitting machine programming , ACM Trans Graph; 38(4): 1–13

	Nardelli, E.S., Vincent, C.C. (2008) Diagnosis and Strategies for a Digital Design Studio , Architecture in ‘computro’ – Integrating Methods and Techniques, proceeding of eCAADe 2008, Antwerp, pp.177-182

	Narian, R., A. Golas, S. Curtis, and M. C. Lin (2009) Aggregate Dynamics for Dense Crowd Simulation , ACM Transactions on Graphics, Vol. 28, Issue 5

	Nass, C. & Lee, K. M. (2003) Designing social presence of actors in human computer interaction , Proceedings of the CHI 2003 Conference on Human Factors in Computing Systems, ACM Press, New York, NY. (pp 289-296)

	Nassetti F, Pasquero C and Zaroukas E. (2019) The Inhuman City: arachnocomputational languages for urban design , Proceeding for the IAAC responsive city conference, Barcelona

	Nassey, C., & Welch, C. (2019) The Moon Village: Strategies and architectures for growth , Proceedings of the International Astronautical Congress, IAC, 2019-October

	National Research Council (1995) Virtual Reality: Scientific and Technological Challenges , Washington D.C.: National Academy Press

	National Research Council (1995) Virtual Reality - scientific and technological challenge , Washington D.C., National Academy Press

	National Research Council. (1999) Our common journey: a transition toward sustainability , Washington, D.C.: National Academies Press

	Nauata N, Chang K, Cheng C, et al (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Eur Conf Computer Vis 2020: 162–177.

	Nauata, N, K. Chang, C. Chin-Yi, G. Mori, and Y. Furukawa (2020) House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation , European Conference on Computer Vision. 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 , 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., Chang, K.-H., Cheng, C.-Y., Mori, G. & Furukawa, Y. (2020) House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation , A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Proceedings of the European Conference on Computer Vision (ECCV) (pp. 162–177). Springer International Publishing. https://doi.org/10.1007/978-3-030-58452-8_10

	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

	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 , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 13627–13636). https://doi.org/10.1109/CVPR46437.2021.01342

	Nault E, Waibel C, Carmeliet J, et al. (2018) Development and test application of the UrbanSOLve decision-support prototype for early-stage neighborhood design , Build Environ; 137: 58–72.

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