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The paper discusses automated solutions for 3D object modeling at multiple resolutions in the context of virtual reality. An original solution, based on an unsupervised neural network, is proposed to guide the creation of selectively densified meshes. A neural gas network, applied over a sparse density object mesh, adapts its nodes during training to capture the embedded shape of the object. Regions...
Simultaneous acquisition of depth and texture information, such as that provided by RGB-D sensors, finds an ever increasing number of applications, including objects modeling, human-machine interfaces, and robot navigation. One of the challenges resulting from the use of densely populated 3D datasets originates from the massive acquisition, management and processing of the data generated. This reality...
Simultaneous acquisition of depth and texture information, such as that provided by RGB-D sensors, finds an ever increasing number of applications, including objects modeling, human-machine interfaces, and robot navigation. One of the challenges resulting from the use of densely populated 3D datasets originates from the massive acquisition, management and processing of the data generated. This reality...
The paper discusses a novel unsupervised learning approach for tracking deformable objects manipulated by a robotic hand in a series of images collected by a video camera. The object of interest is automatically segmented from the initial frame in the sequence. The segmentation is treated as clustering based on color information and spatial features and an unsupervised network is employed to cluster...
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