Browsing by Author "Li, Lei"

Now showing 1 - 4 of 4
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    Context-based Sketch Classification
    (ACM, 2018) Zhang, Jianhui; Chen, Yilan; Li, Lei; Fu, Hongbo; Tai, Chiew-Lan; Aydın, Tunç and Sýkora, Daniel
    We present a novel context-based sketch classification framework using relations extracted from scene images. Most of existing methods perform sketch classification by considering individually sketched objects and often fail to identify their correct categories, due to the highly abstract nature of sketches. For a sketched scene containing multiple objects, we propose to classify a sketched object by considering its surrounding context in the scene, which provides vital cues for resolving its recognition ambiguity. We learn such context knowledge from a database of scene images by summarizing the inter-object relations therein, such as co-occurrence, relative positions and sizes.We show that the context information can be used for both incremental sketch classification and sketch co-classification. Our method outperforms the state-of-the-art single-object classification method, evaluated on a new dataset of sketched scenes.
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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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    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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    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.