[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-650481-105":59,"doc-detail-650481-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","incad-2d-instance-segmentations-for-2d-vector-drawings","INCAD - 2D instance segmentations for 2D vector drawings","","Orthoimages serve as a base for CAD vectorization, but manual vectorization is time-consuming and expert-dependent. Deep learning can generate results automatically, yet training requires large, pixel-level annotated datasets, including for semantic and instance segmentation. The paper addresses this by transforming 2D CAD drawings into a deep-learning dataset (CAD2DLD), leveraging expert-created CAD data. The proposed InCAD workflow trains YOLOv8 instance segmentation and evaluates stone vector drawing generation on a fortification wall, reporting 78.34 mIoU.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/incad-2d-instance-segmentations-for-2d-vector-drawings/650481/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/incad-2d-instance-segmentations-for-2d-vector-drawings/650481.png","ImageObject",300,407,{"name":92,"@type":93},"Kyle","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-24",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is vectorizing orthophotos for CAD time-consuming?","Question",{"text":112,"@type":113},"Because 2D architectural vector drawings are commonly created by manually vectorizing orthophotos using CAD software, which is labour-intensive and expert-dependent.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What dataset issue does the paper address?",{"text":117,"@type":113},"Deep learning instance segmentation needs large amounts of ground truth pixel-level labels, while 2D CAD data is underexplored as a source of training data.",{"name":119,"@type":110,"acceptedAnswer":120},"How does InCAD create 2D vector drawings?",{"text":121,"@type":113},"InCAD automatically creates 2D vector drawings using YOLOv8 instance segmentation trained on data generated from transformed 2D CAD drawings (CAD2DLD).","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},650481,1792877046,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","INCAD: 2D VECTOR DRAWINGS CREATION USING INSTANCE SEGMENTATION  \nThodoris Betsas*1, Andreas Georgopoulos 1, Anastasios Doulamis 1  \n1 National Technical University of Athens, School of Rural , Surveying and Geoinformatics Engineering, Lab of Photogrammetry, Zografou Campus, Heroon Polytechniou 9, 15780, Zografou, Athens, Greece  \n[betsasth@mail.ntua.gr](betsasth@mail.ntua.gr), [drag@central.ntua.gr](drag@central.ntua.gr), [adoulam@cs.ntua.gr](adoulam@cs.ntua.gr)  \nCommission II  \nKEY WORDS: 2D instance segmentation, 2D vector drawings, cultural heritage, YOLOv8, orthophotos ABSTRACT:  \nOrthoimages are a common product used as a base in CAD software for vectorization purposes. In fact, vectorization of orthoimages constitutes a tedious and labour-intensive process which should be supervised by experts e.g. architects, chemical engineers etc. On the one hand, deep learning algorithms are used extensively nowadays achieving high quality results. On the other hand, deep learning algorithms require a huge amount of manually annotated data to be trained on , which is a very difficult process especially at pixel level applications like semantic segmentation and instance segmentation. However, the transformation of 2D CAD drawings into a suitable deep learning dataset (CAD2DLD) is underexplored ignoring a large source of data, created by experts. In this effort, the InCAD algorithm is proposed, which aims to automatically create 2D vector drawings using the YOLOv8 instance segmentation algorithm which was trained on CAD2DLD data. Additionally, a methodology for transforming 2D CAD drawings into a suitable deep learning dataset for instance segmentation, is presented. Finally, the proposed workflow is evaluated on the creation of 2D vector drawings of stones ofa fortification wall achieving promising results (78.34 mIoU) .  \nCAD Drawing Polygons Inference Patches  \nLabels in COCO format YOLO v8  \nFigure 1 : InCAD Flowchart. Orange Rectangle: The transformation of CAD Drawings to COCO labels for training. The COCO labels are used to train the YOLOv8 . Blue Rectangle: The inference Images are passed to YOLOv8  \nalgorithm. Green Rectangle: The output of InCAD algorithm.  \nInCAD Drawings  \n\n| |\n| --- |\n|  |\n\n\n| \u003Cbr>| |\n| --- | --- |\n\n\n| \u003Cbr>| |\n| --- | --- |\n\n1. INTRODUCTION  \nGeometric documentation of cultural heritage buildings could be achieved using either conventional or modern photogrammetric approaches in combination with postprocessing steps, providing many products to the users like textured 3D models, orthophotos and 2D architectural vector drawings. There are many researches investigating the use of deep learning techniques on cultural heritage data for 3D documentation (Agrafiotis, Talaveros and Georgopoulos, 2023), and inspection (Tzortzis et al., 2022) among others, using either conventional RGB images or orthophotographies.  \nOrthophotography combines the valuable information of texture with the ability to perform accurate measurements. However, they are commonly used as raster maps for the creation of 2D vector drawings e.g., architectural, material etc. More precisely, the 2D vector drawings are created by manually vectorizing the orthophotos using Computer Aided Design (CAD) software, which is a time-consuming and labour-intensive process. The output of this process is a CAD drawing file which  \ncontains the vectorized objects assigned to different layers. Consequently, the CAD drawings could provide both geometric i.e., vector like lines, polygons etc. and semantic information i.e., the layer names. To be more specific, each CAD drawing has different layers representing the available data and the vectorized objects. For instance, the available data could be an orthophoto of a fortification wall and the vectorized objects could be the“stones”,“mortar”,“wood” [etc. parts](etc. parts) of the wall. The main idea of this paper is to investigate the use of CAD drawings as a training dataset for deep learning instance segmenta","cbCaiiy3z6zGeuNh","https://ap.wps.com/l/cbCaiiy3z6zGeuNh","pdf",1992926,"English","# INTRODUCTION\n## Problem: Manual vectorization and data-hungry deep learning\n## Proposed approach: InCAD and CAD2DLD for YOLOv8","[{\"question\":\"Why is vectorizing orthophotos for CAD time-consuming?\",\"answer\":\"Because 2D architectural vector drawings are commonly created by manually vectorizing orthophotos using CAD software, which is labour-intensive and expert-dependent.\"},{\"question\":\"What dataset issue does the paper address?\",\"answer\":\"Deep learning instance segmentation needs large amounts of ground truth pixel-level labels, while 2D CAD data is underexplored as a source of training data.\"},{\"question\":\"How does InCAD create 2D vector drawings?\",\"answer\":\"InCAD automatically creates 2D vector drawings using YOLOv8 instance segmentation trained on data generated from transformed 2D CAD drawings (CAD2DLD).\"}]","INCAD - 2D instance segmentations for 2D vector drawings | PDF",1792829498]