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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	acadia19_500
id	acadia19_500
authors	Larsen, Niels Martin; Anders Kruse Aagaard
year	2019
title	Exploring Natural Wood
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 500-509
doi	https://doi.org/10.52842/conf.acadia.2019.500
summary	By investigating methods for using computation and digital manufacturing technologies to integrate material properties with architectural design tools, the research in this paper aims at revealing new potentials for the use of wood in architecture. Through an explorative approach, material particularities and fabrication methods are explored and combined into new workflows and architectural expressions. The research looks into different properties and capacities of wood, but the main part of the experimentation revolves around crooked oak logs. Due to their irregularities, these logs are normally discarded. However, through the methods suggested in this research, they are instead matched with unique processing informed by their divergence. The research presents a workflow for handling the discrete shapes of sawlogs in a system that both involve the collecting of material, scanning/digitization, handling of a stockpile, computer analysis, design, and robotic manufacturing. The workflow includes multiple custom-made solutions for handling the complex and different shapes and data of wood logs in a highly digitized machining and fabrication environment. The suggested method is established through investigations of wood as a natural material, studies of the production lines in the current wood industry, and experimentation in our in-house laboratory facilities. This up-cycling of discarded wood supply establishes a non-standard workflow that utilizes non-standard material stock and leads to a critical articulation of today’s linear material economy. The research thereby gives an example of how the natural forms and properties of sawlogs can be directly used to generate new structures and spatial conditions.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:52

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

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