'CADSketchNet' - An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks. (October 2021)

• Record Type: Journal Article
• Authors: Manda, Bharadwaj; Dhayarkar, Shubham; Mitheran, Sai; Viekash, V.K.; Muthuganapathy, Ramanathan
• Is Part Of: Computers & graphics. Volume 99(2021)
• Page Start: 100
• Page End: 113
• Publication Date: 2021-10
• Subjects: Retrieval -- Search -- Dataset -- Deep Learning -- CAD -- Sketch; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2021.07.001
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: The goal of this paper is to create a sketch dataset that is suitable for developing deep learning-based solutions to the problem of search and retrieval in 3D CAD models. A sketch dataset of query images, called 'CADSketchNet' has been built using the available CAD datasets. For the 58, 696 CAD models of the MCB Dataset, we obtain computer-generated sketches. We call this as Dataset-A. We obtain hand-drawn sketch data for all the 801 CAD models from ESB dataset. This is the Dataset-B. For obtaining Dataset-A, many computer sketch generation methods, including the current state-of-the art methods are experimented. We justify the proposed weighted canny technique based on the comparison. An analysis of the quality of the hand-drawn sketches in Dataset-B is also performed. Various deep learning architectures have been tested on the developed CADSketchNet dataset, and a detailed analysis of their performance have been reported. Among the many experiments conducted, it is shown that a Siamese Network approach yielded the best results as compared to other features/networks. The class-wise results of the approach on both Datasets - A & B. We also justify the need for a dedicated dataset for CAD over the regular 3D graphical shapes by testing the existing methods on the developed CADSketchNet. Graphical abstract: Abstract: Ongoing advancements in the fields of 3D modelling and digital archiving have led to an outburst in the amount of data stored digitally. Consequently, several retrieval systems have been developed depending on the type of data stored in these databases. However, unlike text data or images, performing a search for 3D models is non-trivial. Among 3D models, retrieving 3D Engineering/CAD models or mechanical components is even more challenging due to the presence of holes, volumetric features, presence of sharp edges etc., which make CAD a domain unto itself. The research work presented in this paper aims at developing a dataset suitable for building a retrieval system for 3D CAD models based on deep learning. 3D CAD models from the available CAD databases are collected, and a dataset of computer-generated sketch data, termed 'CADSketchNet', has been prepared. Additionally, hand-drawn sketches of the components are also added to CADSketchNet. Using the sketch images from this dataset, the paper also aims at evaluating the performance of various retrieval system or a search engine for 3D CAD models that accepts a sketch image as the input query. Many experimental models are constructed and tested on CADSketchNet. These experiments, along with the model architecture, choice of similarity metrics are reported along with the search results.