PG2014short

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https://diglib.eg.org/handle/10.2312/7750

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Browsing PG2014short by Subject "Applications"

Now showing 1 - 2 of 2
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    Automatic 3D Posing from 2D Hand-Drawn Sketches
    (The Eurographics Association, 2014) Gouvatsos, Alexandros; Xiao, Zhidong; Marsden, Neil; Zhang, Jian J.; John Keyser and Young J. Kim and Peter Wonka
    Inferring the 3D pose of a character from a drawing is a non-trivial and under-constrained problem. Solving it may help automate various parts of an animation production pipeline such as pre-visualisation. In this paper, a novel way of inferring the 3D pose from a monocular 2D sketch is proposed. The proposed method does not make any external assumptions about the model, allowing it to be used on different types of characters. The 3D pose inference is formulated as an optimisation problem and a parallel variation of the Particle Swarm Optimisation algorithm called PARAC-LOAPSO is utilised for searching the minimum. Testing in isolation as well as part of a larger scene, the presented method is evaluated by posing a lamp and a horse character. The results show that this method is robust and is able to be extended to various types of models.
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    Scene Segmentation and Understanding for Context-Free Point Clouds
    (The Eurographics Association, 2014) Spina, Sandro; Debattista, Kurt; Bugeja, Keith; Chalmers, Alan; John Keyser and Young J. Kim and Peter Wonka
    The continuous development of new commodity hardware intended to capture the surface structure of objects is quickly making point cloud data ubiquitous. Scene understanding methods address the problem of determining the objects present in a point cloud which, dependant on sensor capabilities and object occlusions, is normally noisy and incomplete. In this paper, we propose a novel technique which enables automatic identification of semantically meaningful structures within point clouds acquired using different sensors on a variety of scenes. The representation model, namely the structure graph, with nodes representing planar surface segments, is computed over these point clouds to help with the identification task. In order to accommodate for more complex objects (e.g. chair, couch, cabinet, table), a training process is used to determine and concisely describe, within each object's structure graph, its important shape characteristics. Results on a variety of point clouds show how our method can quickly discern certain object types.