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 2005_083
id 2005_083
authors Agostinho, Francisco Santos
year 2005
title Architecture as Drawing, Perception and Cognition
source Digital Design: The Quest for New Paradigms [23nd eCAADe Conference Proceedings / ISBN 0-9541183-3-2] Lisbon (Portugal) 21-24 September 2005, pp. 83-90
summary This work is about realizing that human perception is inherent to architecture. It is an asset and a trait subject to training and development in an empirical way, involving physical and manual action. It cannot be taught literally through convention and logic reasoning. It is a human achievement of great significance built on intellectual and scientific knowledge. It is something, being physical and empirical, that is supported on instrumental procedure. The computer, as a machine and an instrument, does not shorten the empirical experience of manipulation; on the contrary, it enhances J.J. Gibson’s findings about the perception of space in relation to eye and body movement. Being a cybernetic machine the computer may, and shall, evolve, and become perceptive. In order for that to happen, it is important to keep in mind the mechanism of human perception. Through producing a computerized model of a major architectural work, we develop natural knowledge about its physical features and the thought that lies underneath. To be able to use the computer as an instrument provides a user with explicit knowledge about its ways and mechanism that has to be made available. It involves training, which is to a great extent self-explanatory, and also explicit knowledge about the conventions that are being used, such as programming, reasoning and trigonometry.
keywords Visualization; Environmental Simulation; Knowledge Modelling (KM); 3D Modeling
series eCAADe
last changed 2012/11/23 18:17

_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
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
last changed 2016/03/11 09:21

_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
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
last changed 2016/03/11 09:21

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