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Research map / Detection and segmentation / CNN features

DeepLSD: Line Segment Detection and Refinement With Deep Image Gradients

CVPR 2023 · Detection and segmentation › CNN features · approach: Lane detection · 86 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DeepLSD instead of LSDLine detection · RDNIMhomography estimation: 0.468 -> 0.591Helped
Use DeepLSD instead of LSDLine detection · HPatcheshomography estimation: 0.697 -> 0.705Helped
Use DeepLSD instead of LSDLine detection · Hypersim average3D line recall: 213.9 -> 226.1Helped
Use DeepLSD instead of LSD7Scenes localization · 7Scenes Totalpose accuracy at 5 cm / 5 deg: 83.4 -> 84.0Helped
Detect double edges rather than single edges (ablation)DeepLSD · HPatcheshomography estimation: 0.434 -> 0.705Helped
Add distance-field normalization (ablation)DeepLSD · HPatcheshomography estimation: 0.674 -> 0.705Helped
Replace hybrid LSD extraction with HAWP trained on the pseudo-ground-truth lines (ablation)DeepLSD · HPatcheshomography estimation: 0.705 -> 0.245Hurt
Add line refinement without vanishing-point constraintsHAWP · Wireframehomography estimation: 0.701 -> 0.864Helped
Add line refinement with vanishing-point constraintsTP-LSD · Wireframehomography estimation: 0.658 -> 0.913Helped
Add vanishing-point constraints to line refinement (ablation)HAWP · Wireframehomography estimation: 0.864 -> 0.892Helped
Add vanishing-point constraints to line refinement (ablation)SOLD2 · Wireframehomography estimation: 0.935 -> 0.920Hurt
Add vanishing-point constraints to line refinement (ablation)DeepLSD · Wireframehomography estimation: 0.994 -> 0.981Hurt

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