Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
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Abstract (arXiv)
Parsing Computer-Aided Design (CAD) drawings is a fundamental step for CAD revision, semantic-based management, and the generation of 3D prototypes in both the architecture and engineering industries. Labeling symbols from a CAD drawing is a challenging yet notorious task from a practical point of view. In this work, we propose to label and spot symbols from CAD images that are converted from CAD drawings. The advantage of spotting symbols from CAD images lies in the low requirement of labelers and the low-cost annotation. However, pixel-wise spotting symbols from CAD images is challenging work. We propose a pixel-wise point location via Progressive Gaussian Kernels (PGK) to balance between training efficiency and location accuracy. Besides, we introduce a local offset to the heatmap-based point location method. Based on the keypoints detection, we propose a symbol grouping method to redraw the rectangle symbols in CAD images. We have released a dataset containing CAD images of equipment rooms from telecommunication industrial CAD drawings. Extensive experiments on this real-world dataset show that the proposed method has good generalization ability.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 54 references that are in the map. First posted on arXiv 17 Apr 2024.
Builds on
35 of 54 references.
References · 35
- 2017Attention Is All You Needcited by 59,932
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2016Density estimation using Real NVPcited by 2,029
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
23 more
- 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
- 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
- 2021CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windowscited by 526
- 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
- 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
- 2017Integral Human Pose Regressioncited by 255
- 2014Efficient Object Localization Using Convolutional Networkscited by 226
- 2021Human Pose Regression with Residual Log-likelihood Estimationcited by 131
- 2021TokenPose: Learning Keypoint Tokens for Human Pose Estimationcited by 106
- 2023Real-Time Flying Object Detection with YOLOv8cited by 93
- 2021Lite-HRNet: A Lightweight High-Resolution Networkcited by 91
- 2020Key Points Estimation and Point Instance Segmentation Approach for Lane Detectioncited by 77
- 2018Numerical Coordinate Regression with Convolutional Neural Networkscited by 72
- 2021SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimationcited by 68
- 2019DirectPose: Direct End-to-End Multi-Person Pose Estimationcited by 49
- 2021FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spottingcited by 28
- 2020Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Frameworkcited by 17
- 2022GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawingscited by 13
- 2020Counting People by Estimating People Flowscited by 11
- 2021Localization with Sampling-Argmaxcited by 11
- 2024Symbol as Points: Panoptic Symbol Spotting via Point-based Representationcited by 11
- 2022Heatmap Distribution Matching for Human Pose Estimationcited by 9
- 2023Bias-Compensated Integral Regression for Human Pose Estimationcited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.