# Recurrence-based Vanishing Point Detection

arXiv: https://arxiv.org/abs/2412.20666 · HTML: https://mlsanity.com/paper/2412.20666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://mlsanity.com/paper/1710.06288/) (2017)
- [Learning to Reconstruct 3D Manhattan Wireframes from a Single Image](https://mlsanity.com/paper/1905.07482/) (2019)
- [NeurVPS: Neural Vanishing Point Scanning via Conic Convolution](https://mlsanity.com/paper/1910.06316/) (2019)
- [Deep vanishing point detection: Geometric priors make dataset variations vanish](https://mlsanity.com/paper/2203.08586/) (2022)
- [Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval](https://mlsanity.com/paper/1608.04267/) (2016)
- [Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations](https://mlsanity.com/paper/1911.01507/) (2019)
- [Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images](https://mlsanity.com/paper/1607.07129/) (2016)
- [Vanishing point detection with convolutional neural networks](https://mlsanity.com/paper/1609.00967/) (2016)
- [Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales](https://mlsanity.com/paper/1907.11539/) (2019)
- [Novel 3D Scene Understanding Applications From Recurrence in a Single Image](https://mlsanity.com/paper/2210.07991/) (2022)

## Built on by (most cited first, 1 of 1)

- [MMPerspective: Do MLLMs Understand Perspective? A Comprehensive Benchmark for Perspective Perception, Reasoning, and Robustness](https://mlsanity.com/paper/2505.20426/) (2025)

## Abstract (arXiv)

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.