# Semantic Segmentation of Panoramic Images Using a Synthetic Dataset

Source: [Semantic Segmentation of Panoramic Images Using a Synthetic Dataset](<https://www.sota2.com/research/paper/68fa66411e209131cea0f3af>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:46:30.691Z

Authors: Yuanyou Xu, Kaiwei Wang, Kailun Yang, Dongming Sun, Jia Fu

Published: 2019-09-02T04:10:34.000Z

## Abstract

Panoramic images have advantages in information capacity and scene stability due to their large field of view (FoV). In this paper, we propose a method to synthesize a new dataset of panoramic image. We managed to stitch the images taken from different directions into panoramic images, together with their labeled images, to yield the panoramic semantic segmentation dataset denominated as SYNTHIA-PANO. For the purpose of finding out the effect of using panoramic images as training dataset, we designed and performed a comprehensive set of experiments. Experimental results show that using panoramic images as training data is beneficial to the segmentation result. In addition, it has been shown that by using panoramic images with a 180 degree FoV as training data the model has better performance. Furthermore, the model trained with panoramic images also has a better capacity to resist the image distortion.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.00532>)

[PDF](<https://arxiv.org/pdf/1909.00532.pdf>)