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Item 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, GordonThere 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.Item 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, GordonThe 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.Item 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, GordonWe 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.Item 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, GordonImage 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.