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Deep Virtual Markers for Articulated 3D Shapes
[article]
2021
arXiv
pre-print
We propose deep virtual markers, a framework for estimating dense and accurate positional information for various types of 3D data. We design a concept and construct a framework that maps 3D points of 3D articulated models, like humans, into virtual marker labels. To realize the framework, we adopt a sparse convolutional neural network and classify 3D points of an articulated model into virtual marker labels. We propose to use soft labels for the classifier to learn rich and dense interclass
arXiv:2108.09000v1
fatcat:tirjj6dsyfdpdpmb4utpdh6lz4