Semantic Line Combination Detector
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Abstract (arXiv)
A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First, we generate various line combinations from reliable lines. Second, we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. Moreover, it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection, symmetry axis detection, and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 29 Apr 2024.
Builds on
24 of 40 references.
References · 24
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2015Holistically-Nested Edge Detectioncited by 590
- 2017Fine-tuning CNN Image Retrieval with No Human Annotationcited by 497
- 2016Large-Scale Image Retrieval with Attentive Deep Local Featurescited by 238
- 2018Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarkingcited by 189
- 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
- 2020Keep your Eyes on the Lane: Real-time Attention-guided Lane Detectioncited by 137
- 2016End-to-end Learning of Deep Visual Representations for Image Retrievalcited by 122
- 2022CLRNet: Cross Layer Refinement Network for Lane Detectioncited by 82
- 2023iDisc: Internal Discretization for Monocular Depth Estimationcited by 71
12 more
- 2022CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentationcited by 60
- 2020Deep Hough Transform for Semantic Line Detectioncited by 42
- 2016Horizon Lines in the Wildcited by 30
- 2016Detecting Vanishing Points using Global Image Context in a Non-Manhattan Worldcited by 27
- 2019NeurVPS: Neural Vanishing Point Scanning via Conic Convolutioncited by 24
- 2020Single View Metrology in the Wildcited by 20
- 2016Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrievalcited by 11
- 2016A convolutional approach to reflection symmetrycited by 10
- 2019Temporally Consistent Horizon Linescited by 8
- 2017Wavelet-based Reflection Symmetry Detection via Textural and Color Histogramscited by 7
- 2022Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matchingcited by 4
- 2021Harmonious Semantic Line Detection via Maximal Weight Clique Selectioncited by 3
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