Sketchcleannet -- A Deep Learning Approach To The Enhancement And Correction Of Query Sketches For A 3D CAD Model Retrieval System

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 ske

Stats

• citations15
• S2 citations—
• github stars0
• HF likes0
• heat score9.03
• arxiv keymanda2022sketchcleannet

Related papers

• 'cadsketchnet' -- An Annotated Sketch Dataset For 3D CAD Model Retrieval With Deep Neural Networks (2021)11.19
• Livesketch: Query Perturbations For Guided Sketch-based Visual Search (2019)12.47
• Towards 3D Vr-sketch To 3D Shape Retrieval (2022)7.16
• Sketching Without Worrying: Noise-tolerant Sketch-based Image Retrieval (2022)12.74
• Sketchmate: Deep Hashing For Million-scale Human Sketch Retrieval (2018)15.03
• Weakly-supervised End-to-end CAD Retrieval To Scan Objects (2022)0.00
• Self-supervised Graph Neural Network For Mechanical CAD Retrieval (2024)0.00
• Back To The Drawing Board: Rethinking Scene-level Sketch-based Image Retrieval (2025)0.00