[ { "id": "rasoulzadeh-2024-strokes2surface", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/208007", "title": "Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches", "date": "2024-05", "abstract": "We present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well-connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand-engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5233, 193, 5429, 1799 ], "articleno": "e15054", "doi": "10.1111/cgf.15054", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "2", "pages": "16", "pages_from": "1", "pages_to": "16", "publisher": "WILEY", "volume": "43", "research_areas": [], "keywords": [ "CCS Concepts", "Computer graphics", "Computing methodologies → Artificial intelligence", "Machine learning" ], "weblinks": [], "files": [], "projects_workgroups": [ "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/rasoulzadeh-2024-strokes2surface/", "__class": "Publication" }, { "id": "reisinger-2023-iad", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/188467", "title": "Integrating AEC Domain-Specific Multidisciplinary Knowledge for Informed and Interactive Feedback in Early Design Stages", "date": "2023-10", "abstract": "In the context of digitalization in the industry, a variety of technologies has been developed for system integration and enhanced team collaboration in the Architecture, Engineering and Construction (AEC) industry. Multidisciplinary design requirements are characterized by a high degree of complexity. Early design methods often rely on implicit or experiential design knowledge, whereas contemporary digital design tools mostly reflect domain-specific silo thinking with time-consuming iterative design processes. Yet, the early design stages hold the greatest potential for design optimization. This paper presents a framework of a multidisciplinary computational integration platform for early design stages that enables integration of AEC domain-specific methods from architecture, engineering, mathematics and computer science. The platform couples a semantic integrative mixed reality sketching application to a shape inference machine-learning based algorithm to link methods for different computation, simulation and digital fabrication tasks. A proof of concept of the proposed framework is presented for the use case of a freeform geometry wall. Future research will explore the potential of the framework to be extended to larger building projects with the aim to connect the method into BIM-processes.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1874, 5233, 1487, 240, 1712, 5202, 1799, 193 ], "booktitle": "Advances in Information Technology in Civil and Building Engineering: Proceedings of ICCCBE 2022 - Volume 2", "date_from": "2022-10-26", "date_to": "2022-10-28", "doi": "10.1007/978-3-031-32515-1_12", "event": "19th International Conference on Computing in Civil and Building Engineering (ICCCBE 2022)", "isbn": "978-3-031-32515-1", "lecturer": [ 1874 ], "location": "Cape Town", "pages": "18", "pages_from": "153", "pages_to": "170", "publisher": "Springer", "volume": "358", "research_areas": [ "Modeling" ], "keywords": [ "Integrated Design", "Early Design Stage", "Mixed Reality Sketching", "Shape Inference", "Computational Design", "Integration Platform", "Digital Fabrication" ], "weblinks": [], "files": [], "projects_workgroups": [ "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/reisinger-2023-iad/", "__class": "Publication" }, { "id": "rasoulzadeh-2023-ani", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/189820", "title": "A Novel Integrative Design Framework Combining 4D Sketching, Geometry Reconstruction, Micromechanics Material Modelling, and Structural Analysis", "date": "2023-08", "abstract": "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 used to initiate, develop, and communicate ideas, heavily impact later stages, resulting in the need for rapid feedback to the architectural designer so they can proceed with adequate knowledge about design implications. Accordingly, this paper presents research on a novel integrative design framework based on a recently developed 4D sketching interface, targeted for architectural design as a form-finding tool coupled with three modules: (1) a Geometric Modelling module, which utilises Points2Surf as a machine learning model for automatic surface mesh reconstruction from the point clouds produced by sketches, (2) a Material Modelling module, which predicts the mechanical properties of biocomposites based on multiscale micromechanics homogenisation techniques, and (3) a Structural Analysis module, which assesses the mechanical performance of the meshed structure on the basis of the predicted material properties using finite element simulations. The proposed framework is a step towards using material-informed design already in the early stages of design.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5233, 5303, 5304, 1874, 1799, 5209, 193 ], "articleno": "102074", "doi": "10.1016/j.aei.2023.102074", "issn": "1873-5320", "journal": "Advanced Engineering Informatics", "publisher": "ELSEVIER SCI LTD", "volume": "57", "research_areas": [], "keywords": [ "3D reconstruction", "Biocomposite", "Early-design stage", "Finite element analysis", "Machine learning", "Material-informed", "Micromechanics", "Multiscale modelling", "Sketch-based interface", "Sketch-based modelling", "Structural analysis" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/rasoulzadeh-2023-ani/", "__class": "Publication" }, { "id": "rasoulzadeh-2022-strokes2surface", "type_id": "talk", "tu_id": null, "repositum_id": "20.500.12708/153311", "title": "Strokes2Surface: Recovering Curve Networks from 4D Architectural Design Sketches for Shape Inference", "date": "2022-10-12", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5233, 193, 1799 ], "date_from": "2022-10-10", "date_to": "2022-10-12", "event": "Advance AEC Autumn School", "lecturer": [ 5233 ], "research_areas": [], "keywords": [ "Architectural Geometry", "Concept Design", "Digital Modeling", "Machine Learning", "Curve Networks", "Shape Inference" ], "weblinks": [], "files": [], "projects_workgroups": [ "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/rasoulzadeh-2022-strokes2surface/", "__class": "Publication" } ]