Abstract: Disclosed is a data augmentation-based preference analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a plurality of space images and labeling a class specifying style information corresponding to each of the plurality of space images or acquiring the plurality of space images to which the class is labeled and generating learning data, generating a second space image by changing pixel information included in a first space image within a predetermined range among the plurality of space images and augmenting the learning data, and labeling a class labeled to the first space image, to the second space image.

Abstract: Disclosed is a data augmentation-based space analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a plurality of space images and labeling a class specifying space information corresponding to each of the plurality of space images or acquiring the plurality of space images to which the class is labeled and generating learning data, generating a second space image by changing some or all of pixel information included in a first space image among the plurality of space images and augmenting the learning data, labeling a class labeled to the first space image, to the second space image, and learning a weight of a model designed based on a predetermined image classification algorithm, for deriving a correlation between a space image included in the learning data and a class labeled to each of the space images.

Abstract: Disclosed is a data augmentation-based object analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a first space image including a first object image and generating a second space image by changing pixel information included in the first space image, specifying a bounding box in a region including the first object image in the first space image and labeling a first class specifying the first object image in the bounding box, and primarily learning a weight of a model designed based on a predetermined object detection algorithm, for deriving a correlation between the first object image in the bounding box and the first class, by inputting the first space image to the model, specifying an object image included in a space image based on the correlation.

Abstract: The present disclosure relates to an automatic three-dimensional solid modeling method and program based on a two-dimensional drawing, including at least: acquiring a drawing image; generating a figure by extracting line segments in the drawing image; identifying sub-areas within the figure and identifying spatial types of the sub-areas; and generating a three-dimensional space model by applying height information corresponding to the spatial types of the sub-areas. The present disclosure is convenient in that there is no need to visit the real three-dimensional space in order to implement the corresponding space on a computer, and can remarkably reduce the time required to implement the three-dimensional space.

Abstract: The present disclosure relates to an automatic three-dimensional solid modeling method and program based on a two-dimensional drawing, including at least: acquiring a drawing image; generating a figure by extracting line segments in the drawing image; identifying sub-areas within the figure and identifying spatial types of the sub-areas; and generating a three-dimensional space model by applying height information corresponding to the spatial types of the sub-areas. The present disclosure is convenient in that there is no need to visit the real three-dimensional space in order to implement the corresponding space on a computer, and can remarkably reduce the time required to implement the three-dimensional space.

Abstract: The present disclosure relates to an automatic three-dimensional solid modeling method and program based on a two-dimensional drawing, including at least: acquiring a drawing image; generating a figure by extracting line segments in the drawing image; identifying sub-areas within the figure and identifying spatial types of the sub-areas; and generating a three-dimensional space model by applying height information corresponding to the spatial types of the sub-areas. The present disclosure is convenient in that there is no need to visit the real three-dimensional space in order to implement the corresponding space on a computer, and can remarkably reduce the time required to implement the three-dimensional space.

Abstract: The present disclosure relates to an automatic three-dimensional solid modeling method and program based on a two-dimensional drawing, including at least: acquiring a drawing image; generating a figure by extracting line segments in the drawing image; identifying sub-areas within the figure and identifying spatial types of the sub-areas; and generating a three-dimensional space model by applying height information corresponding to the spatial types of the sub-areas. The present disclosure is convenient in that there is no need to visit the real three-dimensional space in order to implement the corresponding space on a computer, and can remarkably reduce the time required to implement the three-dimensional space.