A computer-implemented method for recognizing a three-dimensional modeled object from a two-dimensional image, comprising: - providing a first set of two-dimensional images rendered from three-dimensional modeled objects, each two-dimensional image of the first set being associated to a label, a three-dimensional modeled object being an object modeled by data allowing the three-dimensional representation of the three-dimensional modeled object; - providing a second set of two-dimensional images not rendered from three-dimensional modeled objects, each two-dimensional image of the second set being associated to a label; - training a neural network model on both first and second sets; - building an index of the two-dimensional images of the first set by extracting a feature vector for each two-dimensional image of the first set, wherein a feature vector is extracted using the trained model, wherein for each two-dimensional image of the first set, the extracting of the feature vector comprises passing the two-dimensional image through successive layers of the neural network, said layers being parameterized with the learnt parameters of the neural network; - providing a similarity metric; - submitting a two-dimensional image depicting at least one object; - extracting a feature vector of the submitted two-dimensional image by using the trained model; - comparing the extracted feature vector of the submitted two-dimensional image with the indexed feature vectors by using the similarity metric; - determining the closest indexed feature vectors that match with the extracted feature vector; - identifying one or more three-dimensional modeled objects linked to the closest indexed feature vectors; and - retrieving a three-dimensional modeled object similar to the said at least one object of the two-dimensional image submitted, the similarity metric used for the comparison being learned with a learning process, the learning process comprising training a similarity model on both first and second sets, each two-dimensional image of the first set being paired with a two-dimensional image of the second set and the labels associated with two-dimensional images of the first and second set comprising at least a similarity information label, the extraction of the feature vector and the learning process of the similarity metric being concomitantly carried out by using a Siamese network which is the neural network model. - providing a first set of two-dimensional images rendered from three-dimensional modeled objects, each two-dimensional image of the first set being associated to a label, a three-dimensional modeled object being an object modeled by data allowing the three-dimensional representation of the three-dimensional modeled object; - providing a second set of two-dimensional images not rendered from three-dimensional modeled objects, each two-dimensional image of the second set being associated to a label; - training a neural network model on both first and second sets; - building an index of the two-dimensional images of the first set by extracting a feature vector for each two-dimensional image of the first set, wherein a feature vector is extracted using the trained model, wherein for each two-dimensional image of the first set, the extracting of the feature vector comprises passing the two-dimensional image through successive layers of the neural network, said layers being parameterized with the learnt parameters of the neural network; - providing a similarity metric; - submitting a two-dimensional image depicting at least one object; - extracting a feature vector of the submitted two-dimensional image by using the trained model; - comparing the extracted feature vector of the submitted two-dimensional image with the indexed feature vectors by using the similarity metric; - determining the closest indexed feature vectors that match with the extracted feature vector; - identifying one or more three-dimensional modeled objects linked to the closest indexed feature vectors; and - retrieving a three-dimensional modeled object similar to the said at least one object of the two-dimensional image submitted, the similarity metric used for the comparison being learned with a learning process, the learning process comprising training a similarity model on both first and second sets, each two-dimensional image of the first set being paired with a two-dimensional image of the second set and the labels associated with two-dimensional images of the first and second set comprising at least a similarity information label, the extraction of the feature vector and the learning process of the similarity metric being concomitantly carried out by using a Siamese network which is the neural network model.