Abstract: Presently disclosed is a method for co-segmenting three-dimensional models represented by sparse and low-rank feature, comprising: pre-segmenting each three-dimensional model of a three-dimensional model class to obtain three-dimensional model patches for the each three-dimensional model; constructing a histogram for the three-dimensional model patches of the each three-dimensional model to obtain a patch feature vector for the each three-dimensional model; performing a sparse and low-rank representation to the patch feature vector for the each three-dimensional model to obtain a representation coefficient and a representation error of the each three-dimensional model; determining a confident representation coefficient for the each three-dimensional model according to the representation coefficient and the representation error of the each three-dimensional model; and clustering the confident representation coefficient of the each three-dimensional model to co-segment the each three-dimensional model respectiv

Abstract: The invention discloses a deep learning based method for three dimensional (3D) model triangular facet feature learning and classifying and an apparatus. The method includes: constructing a deep convolutional neural network (CNN) feature learning model; training the deep CNN feature learning model; extracting a feature from, and constructing a feature vector for, a 3D model triangular facet having no class label, and reconstructing a feature in the constructed feature vector using a bag-of-words algorithm; determining an output feature corresponding to the 3D model triangular facet having no class label according to the trained deep CNN feature learning model and an initial feature corresponding to the 3D model triangular facet having no class label; and performing classification. The method enhances the capability to describe 3D model triangular facets, thereby ensuring the accuracy of 3D model triangular facet feature learning and classifying results.

Abstract: The invention discloses a deep learning based method for three dimensional (3D) model triangular facet feature learning and classifying and an apparatus. The method includes: constructing a deep convolutional neural network (CNN) feature learning model; training the deep CNN feature learning model; extracting a feature from, and constructing a feature vector for, a 3D model triangular facet having no class label, and reconstructing a feature in the constructed feature vector using a bag-of-words algorithm; determining an output feature corresponding to the 3D model triangular facet having no class label according to the trained deep CNN feature learning model and an initial feature corresponding to the 3D model triangular facet having no class label; and performing classification. The method enhances the capability to describe 3D model triangular facets, thereby ensuring the accuracy of 3D model triangular facet feature learning and classifying results.

Abstract: Presently disclosed is a method for co-segmenting three-dimensional models represented by sparse and low-rank feature, comprising: pre-segmenting each three-dimensional model of a three-dimensional model class to obtain three-dimensional model patches for the each three-dimensional model; constructing a histogram for the three-dimensional model patches of the each three-dimensional model to obtain a patch feature vector for the each three-dimensional model; performing a sparse and low-rank representation to the patch feature vector for the each three-dimensional model to obtain a representation coefficient and a representation error of the each three-dimensional model; determining a confident representation coefficient for the each three-dimensional model according to the representation coefficient and the representation error of the each three-dimensional model; and clustering the confident representation coefficient of the each three-dimensional model to co-segment the each three-dimensional model respectiv

Abstract: A method of constructing 3D clothing model based on single image, estimating a 3D model of human body of an inputted image and constructing 3D clothing plane according to the clothing silhouette of the inputted image. The method includes utilizing the 3D clothing plane and the 3D model of human body to generate a smooth 3D clothing model through a deformation algorithm. A decomposition algorithm of intrinsic image is utilized along with a shape-from-shading algorithm to acquire a set of detail information of clothing from the inputted image. A weighted Laplace editing algorithm is utilized to shift the acquired detail information of clothing to the smooth 3D clothing model to yield a final 3D clothing model. A 3D clothing model is used to generate the surface geometry details including folds, wrinkles.

Abstract: A method of constructing 3D clothing model based on single image, estimating a 3D model of human body of an inputted image and constructing 3D clothing plane according to the clothing silhouette of the inputted image. The method includes utilizing the 3D clothing plane and the 3D model of human body to generate a smooth 3D clothing model through a deformation algorithm. A decomposition algorithm of intrinsic image is utilized along with a shape-from-shading algorithm to acquire a set of detail information of clothing from the inputted image. A weighted Laplace editing algorithm is utilized to shift the acquired detail information of clothing to the smooth 3D clothing model to yield a final 3D clothing model. A 3D clothing model is used to generate the surface geometry details including folds, wrinkles.

Abstract: A method of constructing 3D clothing model based on single image, estimating a 3D model of human body of an inputted image and constructing 3D clothing plane according to the clothing silhouette of the inputted image. The method includes utilizing the 3D clothing plane and the 3D model of human body to generate a smooth 3D clothing model through a deformation algorithm. A decomposition algorithm of intrinsic image is utilized along with a shape-from-shading algorithm to acquire a set of detail information of clothing from the inputted image. A weighted Laplace editing algorithm is utilized to shift the acquired detail information of clothing to the smooth 3D clothing model to yield a final 3D clothing model. A 3D clothing model is used to generate the surface geometry details including folds, wrinkles.