151. SketchCleanNet — A deep learning approach to the enhancement and correction of query sketches for a 3D CAD model retrieval system.
- Author
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Manda, Bharadwaj, Kendre, Prasad Pralhad, Dey, Subhrajit, and Muthuganapathy, Ramanathan
- Subjects
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DEEP learning , *ARTIFICIAL neural networks , *COMPUTER vision , *COMPUTER graphics , *ENGINEERING design , *SEARCH algorithms - Abstract
Search and retrieval remains a major research topic in several domains, including computer graphics, computer vision, engineering design, etc. A search engine requires primarily an input search query and a database of items to search from. In engineering, which is the primary context of this paper, the database consists of 3D CAD models, such as washers, pistons, connecting rods, etc. A query from a user is typically in the form of a sketch, which attempts to capture the details of a 3D model. However, sketches have certain typical defects such as gaps, over-drawn portions (multi-strokes), etc. Since the retrieved results are only as good as the input query, sketches need cleaning-up and enhancement for better retrieval results. In this paper, a deep learning approach is proposed to improve or clean the query sketches. Initially, sketches from various categories are analysed in order to understand the many possible defects that may occur. A dataset of cleaned-up or enhanced query sketches is then created based on an understanding of these defects. Consequently, an end-to-end training of a deep neural network is carried out in order to provide a mapping between the defective and the clean sketches. This network takes the defective query sketch as the input and generates a clean or an enhanced query sketch. Qualitative and quantitative comparisons of the proposed approach with other state-of-the-art techniques show that the proposed approach is effective. The results of the search engine are reported using both the defective and enhanced query sketches, and it is shown that using the enhanced query sketches from the developed approach yields improved search results. [Display omitted] • The first learning-based strategy to clean rough query sketches of 3D CAD models • Introduces SketchCleanNet — an end-to-end image translation scheme • SketchCleanNet aims to understand the mapping between rough sketches and clean query images • A novel scheme to calculate the loss is introduced • Dataset Contribution: The resulting enhanced query sketch dataset is made available publicly. • This paper will significantly contribute to the research community and give researchers opportunities to develop new algorithms for search and retrieval of 3D mechanical components. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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