Kent Fujiwara

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We present a novel non-rigid surface registration method that achieves high accuracy and matches characteristic features without manual intervention. The key insight is to consider the entire shape as a collection of local structures that individually undergo rigid transformations to collectively deform the global structure. We realize this locally rigid(More)
We tackle a new challenge of unsupervised categorical 3D reconstruction using images from different instances without any manually assigned key point correspondence. We take advantage of the observations that objects are generally captured from ground-level viewpoints, and that images transform smoothly between some characteristic viewpoints despite the(More)
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