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2014 2nd International Conference on 3D Vision (3DV) (2014)
Tokyo, Japan
Dec. 8, 2014 to Dec. 11, 2014
ISBN: 978-1-4799-7000-1
pp: 353-360
Xiaokun Wu , Max-Planck-Inst. fur Inf., Saarbrucken, Germany
Chuan Li , Utrecht Univ., Utrecht, Netherlands
Michael Wand , Utrecht Univ., Utrecht, Netherlands
Klaus Hildebrandt , Max-Planck-Inst. fur Inf., Saarbrucken, Germany
Silke Jansen , Max-Planck-Inst. fur Inf., Saarbrucken, Germany
Hans-Peter Seidel , Max-Planck-Inst. fur Inf., Saarbrucken, Germany
Texture synthesis is a versatile tool for creating and editing 2D images. However, applying it to 3D content creation is difficult due to the higher demand of model accuracy and the large search space that also contains many implausible shapes. Our paper explores offset statistics for 3D shape retargeting. We observe that the offset histograms between similar 3D features are sparse, in particular for man-made objects such as buildings and furniture. We employ sparse offset statistics to improve 3D shape retargeting (i.e., Rescaling in different directions). We employ a graph-cut texture synthesis method that iteratively stitches model fragments shifted by the detected sparse offsets. The offsets reveal important structural redundancy which leads to more plausible results and more efficient optimization. Our method is fully automatic, while intuitive user control can be incorporated for interactive modeling in real-time. We empirically evaluate the sparsity of offset statistics across a wide range of subjects, and show our statistics based retargeting significantly improves quality and efficiency over conventional MRF models.
Three-dimensional displays, Shape, Solid modeling, Computational modeling, Optimization, Histograms, Geometry,graph-cut, 3D content creation, sparse offset statistics
Xiaokun Wu, Chuan Li, Michael Wand, Klaus Hildebrandt, Silke Jansen, Hans-Peter Seidel, "3D Model Retargeting Using Offset Statistics", 2014 2nd International Conference on 3D Vision (3DV), vol. 01, no. , pp. 353-360, 2014, doi:10.1109/3DV.2014.74
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