We propose a deep learning method to retrieve the most similar 3D well-designed model that our system has seen before, given a rough 3D model or scanned 3D data. We can either use this retrieved model directly or use it as a reference to redesign it for various purposes. Our neural network consists of 3 different neural networks (sub-nets). The first neural network deals with object images (2D projection) and the other two deals with voxel representations of the 3D object. At the last stage, we combine the results of all 3 sub-nets to get the object classification. Furthermore, we use the second to last layer as a feature map to do the feature matching, and return a list of top N most similar well-designed 3D models.
Ruiting Shao, Yang Lei, Jian Fan, Jerry Liu, "3D Shape Retrieval using Volumetric and Image CNNs: A Meta-Algorithmic Approach" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2018, pp 419-1 - 419-6, https://doi.org/10.2352/ISSN.2470-1173.2018.10.IMAWM-419