# Field-of-View IoU for Object Detection in 360deg Images

arXiv: https://arxiv.org/abs/2202.03176 · HTML: https://mlsanity.com/paper/2202.03176/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [IoU Loss for 2D/3D Object Detection](https://mlsanity.com/paper/1908.03851/) (2019)
- [360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images](https://mlsanity.com/paper/1910.01712/) (2019)
- [Object Detection in Equirectangular Panorama](https://mlsanity.com/paper/1805.08009/) (2018)
- [A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video](https://mlsanity.com/paper/1801.10312/) (2018)
- [Making 360circ Video Watchable in 2D: Learning Videography for Click Free Viewing](https://mlsanity.com/paper/1703.00495/) (2017)
- [Unbiased IoU for Spherical Image Object Detection](https://mlsanity.com/paper/2108.08029/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [CRRS: Concentric Rectangles Regression Strategy for Multi-point Representation on Fisheye Images](https://mlsanity.com/paper/2303.14639/) (2023)

## Abstract (arXiv)

360deg cameras have gained popularity over the last few years. In this paper, we propose two fundamental techniques -- Field-of-View IoU (FoV-IoU) and 360Augmentation for object detection in 360deg images. Although most object detection neural networks designed for the perspective images are applicable to 360deg images in equirectangular projection (ERP) format, their performance deteriorates owing to the distortion in ERP images. Our method can be readily integrated with existing perspective object detectors and significantly improves the performance. The FoV-IoU computes the intersection-over-union of two Field-of-View bounding boxes in a spherical image which could be used for training, inference, and evaluation while 360Augmentation is a data augmentation technique specific to 360deg object detection task which randomly rotates a spherical image and solves the bias due to the sphere-to-plane projection. We conduct extensive experiments on the 360indoor dataset with different types of perspective object detectors and show the consistent effectiveness of our method.