不依赖标注数据，用重复模式自动找消失点。
Recurrence-based Vanishing Point Detection
- 利用图像中重复出现的对应关系提取隐式直线，结合显式直线定位消失点。
- 在3200张合成图上性能超越所有对比方法，在1400张真实图上媲美有监督模型。
- 首次构建两个专用数据集，支持无监督消失点检测研究。
传统消失点检测方法仅依赖图像中明显的直线，而近年的深度学习方法需依赖标注数据训练。本文提出一种新的无监督方法——基于循环的消失点检测（R-VPD），通过挖掘图像中重复对应关系发现隐式直线，与显式直线共同定位消失点。同时，我们构建了两个新数据集：1）含3,200个真实消失点和相机参数的合成图像数据集；2）含1,400个人工标注消失点的真实图像数据集。在合成数据集上，我们的方法优于两种经典方法和两种先进深度学习方法；在真实图像上，优于经典方法且与有监督方法性能相当。
原文摘要 · Abstract (English)
Classical approaches to Vanishing Point Detection (VPD) rely solely on the presence of explicit straight lines in images, while recent supervised deep learning approaches need labeled datasets for training. We propose an alternative unsupervised approach: Recurrence-based Vanishing Point Detection (R-VPD) that uses implicit lines discovered from recurring correspondences in addition to explicit lines. Furthermore, we contribute two Recurring-Pattern-for-Vanishing-Point (RPVP) datasets: 1) a Synthetic Image dataset with 3,200 ground truth vanishing points and camera parameters, and 2) a Real-World Image dataset with 1,400 human annotated vanishing points. We compare our method with two classical methods and two state-of-the-art deep learning-based VPD methods. We demonstrate that our unsupervised approach outperforms all the methods on the synthetic images dataset, outperforms the classical methods, and is on par with the supervised learning approaches on real-world images.
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