publications

2024

1. SketchCleanGAN: A generative network to enhance and correct query sketches for improving 3D CAD model retrieval systems

   Kamalesh Kumar Kosalaraman, Prasad Pralhad Kendre, Raghwani Dhaval Manilal, and 1 more author

   Published in Elsevier, Computer & Graphics, 2024

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   Given an input query, a search and retrieval system fetches relevant information from a dataset. In the Engineering domain, such a system is beneficial for tasks such as design reuse. A two-dimensional (2D) sketch is more conducive for an end user to give as a query than a three-dimensional (3D) object. Such query sketches, nevertheless, will inevitably contain defects like incomplete lines, mesh lines, overdrawn areas, missing areas, etc. Since a retrieval system’s results are only as good as the query, it is necessary to improve the query sketches. In this paper, the problem of transforming a defective CAD sketch into a defect-free sketch is addressed using Generative Adversarial Networks (GANs), which, to the best of our knowledge, has not been investigated before. We first create a dataset of 534 hand-drawn sketches by tracing the boundaries of images of CAD models. We then pair the corrected sketches with their corresponding defective sketches and use them for training a C-WGAN (Conditional Wasserstein Generative Adversarial Network), called SketchCleanGAN. We model the transformation from defective to defect-free sketch as a factorization of the defective input sketch and then translate it to the space of defect-free sketch. We propose a three-branch strategy to this problem. Ablation studies and comparisons with other state-of-the-art techniques demonstrate the efficacy of the proposed technique. Additionally, we also contribute to a dataset of around 58000 improved sketches using the proposed framework.

2023

1. SketchCADGAN: A generative approach for completing partially drawn query sketches of engineering shapes to enhance retrieval system performance

   Prasad Pralhad Kendre, Kamalesh Kumar Kosalaraman, Sanjay Santhosh Kumar Jayasree, and 3 more authors

   Published in Elsevier, Computer & Graphics, 2023

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   Retrieval systems are commonly used to find relevant data in large datasets. In engineering, these systems are useful for locating specific engineering shapes in a large dataset of engineering components. When end users want to search for a shape, they prefer a two-dimensional (2D) sketch over a three-dimensional (3D) object. However, users lacking domain knowledge may struggle to generate a complete query sketch and provide a partially completed sketch instead. Retrieving relevant information from partially drawn sketches is difficult because they may have missing edges, partially drawn circles, holes, ovals, etc. Most retrieval systems compare the similarity between the query and items in the database, so incomplete sketches may be ineffective in finding the relevant information. To address this problem with incomplete sketches, we propose a new generative adversarial network called SketchCADGAN. This network uses a two-stage cascaded architecture, with the first network attempting to predict a CAD model image from an incomplete sketch and the second network using the CAD model image to predict a completed sketch. Both networks are trained together adversarially. Our approach is proven more effective than other advanced techniques through qualitative and quantitative comparisons. Furthermore, we present the results of the retrieval system using both partially drawn and completed sketches, and demonstrate that incorporating completed sketches from the suggested cascaded GAN architecture results in improved retrieval performance.

   @article{KENDRE202355,
     title = {SketchCADGAN: A generative approach for completing partially drawn query sketches of engineering shapes to enhance retrieval system performance},
     journal = {Computers & Graphics},
     volume = {115},
     pages = {55-68},
     year = {2023},
     issn = {0097-8493},
     doi = {https://doi.org/10.1016/j.cag.2023.06.028},
     author = {Kendre, Prasad Pralhad and Kosalaraman, Kamalesh Kumar and Jayasree, Sanjay Santhosh Kumar and Rajan, Sreehari and Jayan, Akash and Muthuganapathy, Ramanathan},
     keywords = {Search and retrieval, Generative adversarial networks(GANs), Sketch completion, Sketch inpainting, CAD model sketches, Incomplete sketches},
   }