# Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

arXiv: https://arxiv.org/abs/1608.04267 · HTML: https://mlsanity.com/paper/1608.04267/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2016.

## Built on by (most cited first, 11 of 11)

- [DepthCues: Evaluating Monocular Depth Perception in Large Vision Models](https://mlsanity.com/paper/2411.17385/) (2024)
- [Deep vanishing point detection: Geometric priors make dataset variations vanish](https://mlsanity.com/paper/2203.08586/) (2022)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching](https://mlsanity.com/paper/2203.15285/) (2022)
- [SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios](https://mlsanity.com/paper/2301.07279/) (2023)
- [Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection](https://mlsanity.com/paper/2303.14311/) (2023)
- [Computational Scaffolding of Composition, Value, and Color for Disciplined Drawing](https://mlsanity.com/paper/2509.17268/) (2025)
- [Novel 3D Scene Understanding Applications From Recurrence in a Single Image](https://mlsanity.com/paper/2210.07991/) (2022)
- [Semantic Line Combination Detector](https://mlsanity.com/paper/2404.18399/) (2024)
- [TopView: Vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning](https://mlsanity.com/paper/2412.16229/) (2024)
- [Recurrence-based Vanishing Point Detection](https://mlsanity.com/paper/2412.20666/) (2024)

## Abstract (arXiv)

Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.