用1000万张无标注图像训练出更准更快的线段检测模型
ScaleLSD: Scalable Deep Line Segment Detection Streamlined
- 基于自监督学习重构线段检测框架，实现高效端到端训练
- 在自然图像上检测线段数量超经典非深度方法，几何表征更完整
- 零样本测试下多任务表现超越传统方法，适合大规模图像几何分析
本文研究图像中线段检测（LSD）问题，旨在构建一个适用于任意自然图像的领域无关鲁棒检测模型。聚焦于可扩展的自监督学习，我们重新审视并简化了深度与非深度LSD方法的基本设计，提出高效高性能的ScaleLSD模型，从超过1000万张未标注的真实世界图像中规模化提取线段几何信息。ScaleLSD在任意自然图像上检测的线段数量显著超过开创性的非深度LSD方法，实现了更完整、更准确的图像几何刻画。实验表明，该模型在零样本条件下，在线段检测、单视图3D几何估计、双视图线段匹配及多视图3D线映射等任务上均表现优异。评估显示，ScaleLSD是首个在所有测试维度上全面超越经典非深度LSD的深度方法，显著拓展并强化了图像线几何的实用性。代码与模型开源于https://github.com/ant-research/scalelsd。
原文摘要 · Abstract (English)
This paper studies the problem of Line Segment Detection (LSD) for the characterization of line geometry in images, with the aim of learning a domain-agnostic robust LSD model that works well for any natural images. With the focus of scalable self-supervised learning of LSD, we revisit and streamline the fundamental designs of (deep and non-deep) LSD approaches to have a high-performing and efficient LSD learner, dubbed as ScaleLSD, for the curation of line geometry at scale from over 10M unlabeled real-world images. Our ScaleLSD works very well to detect much more number of line segments from any natural images even than the pioneered non-deep LSD approach, having a more complete and accurate geometric characterization of images using line segments. Experimentally, our proposed ScaleLSD is comprehensively testified under zero-shot protocols in detection performance, single-view 3D geometry estimation, two-view line segment matching, and multiview 3D line mapping, all with excellent performance obtained. Based on the thorough evaluation, our ScaleLSD is observed to be the first deep approach that outperforms the pioneered non-deep LSD in all aspects we have tested, significantly expanding and reinforcing the versatility of the line geometry of images. Code and Models are available at https://github.com/ant-research/scalelsd
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