Browsing by Author "Wang, Wencheng"

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    Contracting Medial Surfaces Isotropically for Fast Extraction of Centred Curve Skeletons
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Li, Lei; Wang, Wencheng; Chen, Min and Zhang, Hao (Richard)
    Curve skeletons, which are a compact representation for three‐dimensional shapes, must be extracted such that they are high quality, centred and smooth. However, the centredness measurements in existing methods are expensive, lowering the extraction efficiency. Although some methods trade quality for acceleration, their generated low‐quality skeletons are not suitable for applications. In this paper, we present a method to quickly extract centred curve skeletons. It operates by contracting the medial surface isotropically to the locus of the centres of its maximal inscribed spheres, which are spheres that have their centres on the medial surface and cannot be further enlarged while remaining the boundary of their intersections with the medial surface composed of only the points on the sphere surfaces. Thus, the centred curve skeleton can be extracted conveniently. For fast extraction, we develop novel measures to quickly generate the medial surface and contract it layer by layer, with every layer contracted isotropically using spheres of equal radii to account for every part of the medial surface boundary. The experimental results show that we can stably extract curve skeletons with higher centredness and at much higher speeds than existing methods, even for noisy shapes.Curve skeletons, which are a compact representation for three‐dimensional shapes, must be extracted such that they are high quality, centred and smooth. However, the centredness measurements in existing methods are expensive, lowering the extraction efficiency. Although some methods trade quality for acceleration, their generated low‐quality skeletons are not suitable for applications. In this paper, we present a method to quickly extract centred curve skeletons. It operates by contracting the medial surface isotropically to the locus of the centres of its maximal inscribed spheres, which are spheres that have their centres on the medial surface and cannot be further enlarged while remaining the boundary of their intersections with the medial surface composed of only the points on the sphere surfaces.
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    Generating High-quality Superpixels in Textured Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Zhe; Xu, Panpan; Chang, Jian; Wang, Wencheng; Zhao, Chong; Zhang, Jian Jun; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Superpixel segmentation is important for promoting various image processing tasks. However, existing methods still have difficulties in generating high-quality superpixels in textured images, because they cannot separate textures from structures well. Though texture filtering can be adopted for smoothing textures before superpixel segmentation, the filtering would also smooth the object boundaries, and thus weaken the quality of generated superpixels. In this paper, we propose to use the adaptive scale box smoothing instead of the texture filtering to obtain more high-quality texture and boundary information. Based on this, we design a novel distance metric to measure the distance between different pixels, which considers boundary, color and Euclidean distance simultaneously. As a result, our method can achieve high-quality superpixel segmentation in textured images without texture filtering. The experimental results demonstrate the superiority of our method over existing methods, even the learning-based methods. Benefited from using boundaries to guide superpixel segmentation, our method can also suppress noise to generate high-quality superpixels in non-textured images.
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    Image Composition of Partially Occluded Objects
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Tan, Xuehan; Xu, Panpan; Guo, Shihui; Wang, Wencheng; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Image composition extracts the content of interest (COI) from a source image and blends it into a target image to generate a new image. In the majority of existing works, the COI is manually extracted and then overlaid on top of the target image. However, in practice, it is often necessary to deal with situations in which the COI is partially occluded by the target image content. In this regard, both tasks of extracting the COI and cropping its occluded part require intensive user interactions, which are laborious and seriously reduce the composition efficiency. This paper addresses the aforementioned challenges by proposing an efficient image composition method. First, we extract the semantic contents of the images by using state-of-the-art deep learning methods. Therefore, the COI can be selected with clicks only, which can greatly reduce the demanded user interactions. Second, according to the user's operations (such as translation or scale) on the COI, we can effectively infer the occlusion relationships between the COI and the contents of the target image. Thus, the COI can be adaptively embedded into the target image without concern about cropping its occluded part. Therefore, the procedures of content extraction and occlusion handling can be significantly simplified, and work efficiency is remarkably improved. Experimental results show that compared to existing works, our method can reduce the number of user interactions to approximately one-tenth and increase the speed of image composition by more than ten times.
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    Improved Use of LOP for Curve Skeleton Extraction
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Li, Lei; Wang, Wencheng; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    It remains a challenge to robustly and rapidly extract high quality curve skeletons from 3D models of closed surfaces, especially when there are nearby surface sheets. In this paper, we address this challenge by improving the use of LOP (Locally Optimal Projection) to adaptively contract medial surfaces of 3D models. LOP was originally designed to optimize a raw scanned point cloud to its corresponding geometry surface. It has the effect of contraction, and the contraction amplitude is controlled by a support radius. Our improvements are twofold. First, we constrain the LOP operator applied in the 2D medial surface instead of in the 3D space and take a local region growing strategy to find neighborhoods for implementing LOP. Thus, we avoid interference between disconnected surface parts and accelerate the process due to the reduced search space. Second, we adaptively adjust the support radii to have different parts of the medial surface contracted adaptively and synchronously for generating connected skeletal curves. In this paper, we demonstrate that our method allows for each part of the medial surface to be contracted symmetrically to its center line and is insensitive to surface noises. Thus, with our method, centered and connected high quality curve skeletons can be extracted robustly and rapidly, even for models with nearby surface sheets. Experimental results highlight the effectiveness and high efficiency of the method, even for noisy and topologically complex models, making it superior to other state-of-the-art methods.
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    Intrinsic Symmetry Detection on 3D Models with Skeleton-guided Combination of Extrinsic Symmetries
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wang, Wencheng; Ma, Junhui; Xu, Panpan; Chu, Yiyao; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    The existing methods for intrinsic symmetry detection on 3D models always need complex measures such as geodesic distances for describing intrinsic geometry and statistical computation for finding non-rigid transformations to associate symmetrical shapes. They are expensive, may miss symmetries, and cannot guarantee their obtained symmetrical parts in high quality. We observe that only extrinsic symmetries exist between convex shapes, and two intrinsically symmetric shapes can be determined if their belonged convex sub-shapes are symmetrical to each other correspondingly and connected in a similar topological structure. Thus, we propose to decompose the model into convex parts, and use the similar structures of the skeleton of the model to guide combination of extrinsic symmetries between convex parts for intrinsic symmetry detection. In this way, we give up statistical computation for intrinsic symmetry detection, and avoid complex measures for describing intrinsic geometry. With the similar structures being from small to large gradually, we can quickly detect multi-scale partial intrinsic symmetries in a bottom up manner. Benefited from the well segmented convex parts, our obtained symmetrical parts are in high quality. Experimental results show that our method can find many more symmetries and runs much faster than the existing methods, even by several orders of magnitude.
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    Out-of-core Extraction of Curve Skeletons for Large Volumetric Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Chu, Yiyao; Wang, Wencheng; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Existing methods for skeleton extraction have limitations in terms of the amount of memory space available, as the model must be allocated to the random access memory. This challenges the treatment of out-of-core models. Although applying out-of-core simplification methods to the model can fit in memory, this would induce distortion of the model surface, and so causing the skeleton to be off-centered or changing the topological structure. In this paper, we propose an efficient out-of-core method for extracting skeletons from large volumetric models. The method takes a volumetric model as input and first computes an out-of-core distance transform. With the distance transform, we generate a medial mesh to capture the prominent features for skeleton extraction, which significantly reduces the data size and facilitates the process of large models. At last, we contract the medial mesh in an out-of-core fashion to generate the skeleton. Experimental results show that our method can efficiently extract high-quality curve skeletons from large volumetric models with small memory usage.
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    Polycube Shape Space
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhao, Hui; Li, Xuan; Wang, Wencheng; Wang, Xiaoling; Wang, Shaodong; Lei, Na; Gu, Xianfeng; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    There are many methods proposed for generating polycube polyhedrons, but it lacks the study about the possibility of generating polycube polyhedrons. In this paper, we prove a theorem for characterizing the necessary condition for the skeleton graph of a polycube polyhedron, by which Steinitz's theorem for convex polyhedra and Eppstein's theorem for simple orthogonal polyhedra are generalized to polycube polyhedra of any genus and with non-simply connected faces. Based on our theorem, we present a faster linear algorithm to determine the dimensions of the polycube shape space for a valid graph, for all its possible polycube polyhedrons. We also propose a quadratic optimization method to generate embedding polycube polyhedrons with interactive assistance. Finally, we provide a graph-based framework for polycube mesh generation, quadrangulation, and all-hex meshing to demonstrate the utility and applicability of our approach.
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    Topology Preserving Simplification of Medial Axes in 3D Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Chu, Yiyao; Hou, Fei; Wang, Wencheng; Li, Lei; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    We propose an efficient method for topology-preserving simplification of medial axes of 3D models. Existing methods either cannot preserve the topology during medial axes simplification or have the problem of being geometrically inaccurate or computationally expensive. To tackle these issues, we restrict our topology-checking to the areas around the topological holes to avoid unnecessary checks in other areas. Our algorithm can keep high precision even when the medial axis is simplified to be in very few vertices. Furthermore, we parallelize the medial axes simplification procedure to enhance the performance significantly. Experimental results show that our method can preserve the topology with highly efficient performance, much superior to the existing methods in terms of topology preservation, accuracy and performance.