Browsing by Author "Yang, Yin"

Now showing 1 - 5 of 5
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    Automatic Mechanism Modeling from a Single Image with CNNs
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Lin, Minmin; Shao, Tianjia; Zheng, Youyi; Ren, Zhong; Weng, Yanlin; Yang, Yin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high-quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter-part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high-quality 3D mechanism models without user guidance.
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    InShaDe: Invariant Shape Descriptors for Visual Analysis of Histology 2D Cellular and Nuclear Shapes
    (The Eurographics Association, 2020) Agus, Marco; Al-Thelaya, Khaled; Cali, Corrado; Boido, Marina M.; Yang, Yin; Pintore, Giovanni; Gobbetti, Enrico; Schneider, Jens; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
    We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from histology images. The framework is based on a novel shape descriptor of closed contours relying on a geodesically uniform resampling of discrete curves to allow for discrete differential-geometry-based computation of unsigned curvature at vertices and edges. Our descriptor is, by design, invariant under translation, rotation and parameterization. Moreover, it additionally offers the option for uniform-scale-invariance. The optional scale-invariance is achieved by scaling features to z-scores, while invariance under parameterization shifts is achieved by using elliptic Fourier analysis (EFA) on the resulting curvature vectors. These invariant shape descriptors provide an embedding into a fixed-dimensional feature space that can be utilized for various applications: (i) as input features for deep and shallow learning techniques; (ii) as input for dimension reduction schemes for providing a visual reference for clustering collection of shapes. The capabilities of the proposed framework are demonstrated in the context of visual analysis and unsupervised classification of histology images.
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    Online Global Non-rigid Registration for 3D Object Reconstruction Using Consumer-level Depth Cameras
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Xu, Jiamin; Xu, Weiwei; Yang, Yin; Deng, Zhigang; Bao, Hujun; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    We investigate how to obtain high-quality 360-degree 3D reconstructions of small objects using consumer-level depth cameras. For many homeware objects such as shoes and toys with dimensions around 0.06 - 0:4 meters, their whole projections, in the hand-held scanning process, occupy fewer than 20% pixels of the camera's image. We observe that existing 3D reconstruction algorithms like KinectFusion and other similar methods often fail in such cases even under the close-range depth setting. To achieve high-quality 3D object reconstruction results at this scale, our algorithm relies on an online global non-rigid registration, where embedded deformation graph is employed to handle the drifting of camera tracking and the possible nonlinear distortion in the captured depth data. We perform an automatic target object extraction from RGBD frames to remove the unrelated depth data so that the registration algorithm can focus on minimizing the geometric and photogrammetric distances of the RGBD data of target objects. Our algorithm is implemented using CUDA for a fast non-rigid registration. The experimental results show that the proposed method can reconstruct high-quality 3D shapes of various small objects with textures.
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    Pacific Graphics 2022 - Short Papers, Posters, and Work-in-Progress Papers: Frontmatter
    (The Eurographics Association, 2022) Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
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    Stress‐Constrained Thickness Optimization for Shell Object Fabrication
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Zhao, Haiming; Xu, Weiwei; Zhou, Kun; Yang, Yin; Jin, Xiaogang; Wu, Hongzhi; Chen, Min and Zhang, Hao (Richard)
    We present an approach to fabricate shell objects with thickness parameters, which are computed to maintain the user‐specified structural stability. Given a boundary surface and user‐specified external forces, we optimize the thickness parameters according to stress constraints to extrude the surface. Our approach mainly consists of two technical components: First, we develop a patch‐based shell simulation technique to efficiently support the static simulation of extruded shell objects using finite element methods. Second, we analytically compute the derivative of stress required in the sensitivity analysis technique to turn the optimization into a sequential linear programming problem. Experimental results demonstrate that our approach can optimize the thickness parameters for arbitrary surfaces in a few minutes and well predict the physical properties, such as the deformation and stress of the fabricated object.We present an approach to fabricate shell objects with thickness parameters, which are computed to maintain the user‐specified structural stability. Given a boundary surface and user‐specified external forces, we optimize the thickness parameters according to stress constraints to extrude the surface. Our approach mainly consists of two technical components: First, we develop a patch‐based shell simulation technique to efficiently support the static simulation of extruded shell objects using finite element methods. Second, we analytically compute the derivative of stress required in the sensitivity analysis technique to turn the optimization into a sequential linear programming problem.