Cumincad : CUMINCAD Papers : 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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_id	caadria2016_445
id	caadria2016_445
authors	Silvestre, Joaquim; Franc?ois Gue?na and Yasushi Ikeda
year	2016
title	Edition-Oriented 3D Model Rebuilt from Photography
doi	https://doi.org/10.52842/conf.caadria.2016.445
source	Living Systems and Micro-Utopias: Towards Continuous Designing, Proceedings of the 21st International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2016) / Melbourne 30 March–2 April 2016, pp. 445-454
summary	The topic of this paper is about a technique to turn pictures into an intuitively modifiable 3D model. The research employs an analytical method using algorithms to conceptualise and digital- ise architectural spaces in order to highlight parametric shapes. Usual- ly, from one group of digital photos, photogrammetry techniques pro- duce a 3D-model mesh through a high-density 3D point cloud. This discordance between our intuitive partitioning of the mesh and its bare polygonal structure makes it interact poorly compared to the af- fordance of shape and component in our daily experience. Through a capture device, a visualisation of architecture in a digital data form is produced. They are processed by computer vision algorithms and ma- chine learning systems in order to be refined into a parametric model. Parametric elements can be described as a compound of formulas and parameters. By keeping the formula and changing the parameters, the- se elements can be easily modified in a range of likenesses. After be- ing detected during scans, these shapes can be adapted to fit the inten- tion of the designer during the design phase.
keywords	Photogrammetry; convolutional neural network; 3D model; design tool
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2016_881
id	caadria2016_881
authors	Silvestre, Joaquim; Yasushi Ikeda and Franc?ois Gue?na
year	2016
title	Artificial Imagination of Architecture with Deep Convolutional Neural Network
doi	https://doi.org/10.52842/conf.caadria.2016.881
source	Living Systems and Micro-Utopias: Towards Continuous Designing, Proceedings of the 21st International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2016) / Melbourne 30 March–2 April 2016, pp. 881-890
summary	This paper attempts to determine if an Artificial Intelli- gence system using deep convolutional neural network (ConvNet) will be able to “imagine” architecture. Imagining architecture by means of algorithms can be affiliated to the research field of generative archi- tecture. ConvNet makes it possible to avoid that difficulty by automat- ically extracting and classifying these rules as features from large ex- ample data. Moreover, image-base rendering algorithms can manipu- late those abstract rules encoded in the ConvNet. From these rules and without constructing a prior 3D model, these algorithms can generate perspective of an architectural image. To conclude, establishing shape grammar with this automated system opens prospects for generative architecture with image-base rendering algorithms.
keywords	Machine learning; convolutional neural network; generative design; image-based rendering
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

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