2,554 research outputs found

    A Survey of Sketch Based Modeling Systems

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    Sketching maps. Comparison between digital diagrammatic sketches of urban connectivity and actual maps of landscape fabric

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    Digital Participatory Platforms (DPPs) are tools allowing general members of the public to express themselves through design actions. This field is rapidly expanding and has the potential to democratize SS theory, making it visible and relevant to many. Tools that allow participants to develop simple diagrams of urban form can be of help since these types of drawings are easy to make and relate directly to some of the abstractions behind SS theory. However, even if we general members of the public can develop these drawings, the relation between these types of drawings and the reality they may intend to represent has not been mapped sp far. To address this issue we propose an experiment where we compare 200 drawings produced by professionals as part of a participatory process with real scale maps of London parks. We develop an analytic method for the lines of these two datasets using geometric feature extraction and dimensionality reduction representation in a t-SNE scatter graph. Results indicate that, for some types of landscapes, the algorithm effectively matches sketches and map morphologies. In other cases, the geometries of sketches and maps of some landscapes are inherently different since designers tend to develop “cartoons” of their designs, forcing curvature of items or forgetting small details which end up being added into the design in later stages. This would suggest the need to develop sophisticated layers of detail in addition to digital tools if they are to adequately translate between a syntactic approach to design and real-life map results

    Sketching maps Comparison between digital diagrammatic sketches of urban connectivity and actual maps of landscape fabric

    Get PDF
    Digital Participatory Platforms (DPPs) are tools allowing general members of the public to express themselves through design actions. This field is rapidly expanding and has the potential to democratize SS theory, making it visible and relevant to many. Tools that allow participants to develop simple diagrams of urban form can be of help since these types of drawings are easy to make and relate directly to some of the abstractions behind SS theory. However, even if we general members of the public can develop these drawings, the relation between these types of drawings and the reality they may intend to represent has not been mapped sp far. To address this issue we propose an experiment where we compare 200 drawings produced by professionals as part of a participatory process with real scale maps of London parks. We develop an analytic method for the lines of these two datasets using geometric feature extraction and dimensionality reduction representation in a t-SNE scatter graph. Results indicate that, for some types of landscapes, the algorithm effectively matches sketches and map morphologies. In other cases, the geometries of sketches and maps of some landscapes are inherently different since designers tend to develop “cartoons” of their designs, forcing curvature of items or forgetting small details which end up being added into the design in later stages. This would suggest the need to develop sophisticated layers of detail in addition to digital tools if they are to adequately translate between a syntactic approach to design and real-life map results

    Multi-modal Machine Learning in Engineering Design: A Review and Future Directions

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    In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML:multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed

    Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

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    In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model constructionComment: 31 pages, 26 figure

    Freeform User Interfaces for Graphical Computing

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    報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専
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