computer-vision-5

Line Segment Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in line-segment-detection-5

Trend

Dataset

Best Model

Actions

York Urban Dataset

wireframe dataset

Libraries

i

Use these libraries to find line-segment-detection-5 models and implementations

Datasets

Wireframe

KAIST Urban

York Urban Line Segment Database

WaterScenes

BN-HTRd

Subtasks

No subtasks available.

Most implemented papers

Fast 3D Line Segment Detection From Unorganized Point Cloud

Xiaohu Lu, Yahui Liu, Kai Li•Mon Jan 07 2019

This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud based on point cloud segmentation and 2D line detection.

78

Content

ICDAR 2021

0

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TP-LSD: Tri-Points Based Line Segment Detector

Pengfei Xiong, Fangbo Qin, N. Ding, Yijia He, Xiao Liu•Thu Sep 10 2020

This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed, and introduces the tri-points representation, converting the line segment detection to the end-to-end prediction of a root-point and two endpoints for each line segment.

65 0

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ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras

Hao Li, S. Scherer, Wen Yang, Lei Yu, Huai Yu•Thu Nov 05 2020

Experimental results validate the superiority of the proposed ULSD to the SOTA methods both in accuracy and efficiency (40.6fps for pinhole images).

33 0

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Line Segment Detection Using Transformers without Edges

Z. Tu, Weijian Xu, Yifan Xu, David Cheung•Tue Jan 05 2021

A joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free and takes advantages of having integrated tokenized queries, a self-attention mechanism, and encoding-decoding strategy within Transformers.

130 0

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Fully Convolutional Line Parsing

Yi Ma, Xili Dai, Xiaojun Yuan, Hai-gang Gong•Wed Apr 21 2021

This work presents a one-stage Fully Convolutional Line Parsing network (F-Clip) that detects line segments from images and achieves a significantly better trade-off between efficiency and accuracy, resulting in a real-time line detector at up to 73 FPS on a single GPU.

57 0

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Towards Light-Weight and Real-Time Line Segment Detection

ByungSoo Ko, Geonmo Gu, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin•Mon May 31 2021

This paper proposes a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD), an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods.

90 0

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Learning Attraction Field Representation for Robust Line Segment Detection

Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Nan Xue, S. Bai, Fudong Wang•Tue Dec 04 2018

A region-partition based attraction field dual representation for line segment maps, which poses the problem of line segment detection (LSD) as the region coloring problem and harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution.

137 0

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End-to-End Wireframe Parsing

Yichao Zhou, Haozhi Qi, Y. Ma•Tue May 07 2019

This work presents a conceptually simple yet effective algorithm that significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms and proposes a new metric for wireframe evaluation that penalizes overlapped line segments and incorrect line connectivities.

185 0

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PPGNet: Learning Point-Pair Graph for Line Segment Detection

Shenghua Gao, Jia Zheng, Yanyu Xu, Weixin Luo, Kun Huang, N. Bi, Ziheng Zhang, Zhengxin Li, Jinlei Wang•Wed May 08 2019

This paper proposes to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods and introduces the PPGNet, a convolutional neural network that directly infers a graph from an image.

89 0

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