Fig 6 - uploaded by Valentin Senk
Content may be subject to copyright.
Virtual point cloud creation from sketch. (a) original sketch and (b) the obtained point cloud through ray casting by pinhole camera. The point cloud on the right is much denser while capturing the solidness of the sketch compared to the one in Fig. 5.

Virtual point cloud creation from sketch. (a) original sketch and (b) the obtained point cloud through ray casting by pinhole camera. The point cloud on the right is much denser while capturing the solidness of the sketch compared to the one in Fig. 5.

Source publication
Article
Full-text available
State-of-the-art workflows within Architecture, Engineering, and Construction (AEC) are still caught in sequential planning processes. Digital design tools in this domain often lack proper communication between different stages of design and relevant domain knowledge. Furthermore, decisions made in the early stages of design, where sketching is use...

Contexts in source publication

Context 1
... the whole sketch into a solid and, accordingly, matches to the type of point clouds that Points2Surf is trained on. Secondly, since point clouds are obtained from triangle strips, setting the resolution of the pinhole camera can lead to a very dense point cloud independent of drawing speed and sampled points within Unity's update frame rate. Fig. 6 depicts the point cloud of the same sketch as the one in Fig. 6 obtained with the newly devised ...
Context 2
... the type of point clouds that Points2Surf is trained on. Secondly, since point clouds are obtained from triangle strips, setting the resolution of the pinhole camera can lead to a very dense point cloud independent of drawing speed and sampled points within Unity's update frame rate. Fig. 6 depicts the point cloud of the same sketch as the one in Fig. 6 obtained with the newly devised ...

Similar publications

Article
Full-text available
The present research focuses on proposing a novel theoretical micromechanical model (TMM) designed to derive the frequency-dependent storage and loss moduli of woven fabric (WF)-matrix composites, as well as WF-particulate matrix (Hybrid) composites, based on their constituent properties. The TMM serves as a higher-order modulus operator, accountin...

Citations

... Ampanavos et al. [126] carried out similar work using a convolution neural network for the development of structural floor layouts in the initial design phase. Rasoulzadeh et al. [127] sought to fully integrate early design stage workflows between architectural, engineering, and construction teams with a 4D sketching interface that comprises geometric modeling, material modeling, and structural analysis modules. The three modules create a framework for reconstructing architectural forms from sketches, predicting the mechanical behavior of materials, and assessing the form and materials based on finite element simulations. ...
Article
Full-text available
Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spatial and temporal scales of the models increase. This is particularly relevant as the demand for higher fidelity models and simulations increases. Recently, the rapid developments in artificial intelligence technologies, coupled with the wide availability of computational resources and data, have driven the extensive adoption of machine learning techniques to improve the computational accuracy and precision of simulations, which enhances their practicality and potential. In this paper, we present a comprehensive survey of the methodologies and techniques used in this context to solve computationally demanding problems, such as structural system identification, structural design, and prediction applications. Specialized deep neural network algorithms, such as the enhanced probabilistic neural network, have been the subject of numerous articles. However, other machine learning algorithms, including neural dynamic classification and dynamic ensemble learning, have shown significant potential for major advancements in specific applications of structural engineering. Our objective in this paper is to provide a state-of-the-art review of machine learning-based modeling in structural engineering, along with its applications in the following areas: (i) computational mechanics, (ii) structural health monitoring, (iii) structural design and manufacturing, (iv) stress analysis, (v) failure analysis, (vi) material modeling and design, and (vii) optimization problems. We aim to offer a comprehensive overview and provide perspectives on these powerful techniques, which have the potential to become alternatives to conventional modeling methods.