Direct Sampling of Multiview Line Drawings for Document Retrieval

Engineering drawings, scientific data, and governmental document repositories rely on degraded two-dimensional images to represent physical three-dimensional objects. The collection of two-dimensional multiview images are generated from a set of known camera positions that are aimed directly at the target object. These images provide a convenient method for representing the original physical object but significantly degrades the interpretability of the object. The multiview images from the document repositories may be integrated to reconstruct an approximation of the original physical object as a point cloud. We show that retrieval methods for documents are improved by directly sampling point clouds from the multiview image set to reconstruct the original physical object. We compare the retrieval results from direct image retrieval, multiview convolutional neural networks (MVCNN), and point clouds reconstructed from sampled images. To evaluate these models, we trained them on line drawings generated from models in the ShapeNet Core data set. We show retrieval of the reconstructed object is more accurate than single image retrieval or the multiview image set retrieval.