# Generalized Shortest Path-based Superpixels for 3D Spherical Image Segmentation

arXiv: https://arxiv.org/abs/2509.19895 · HTML: https://mlsanity.com/paper/2509.19895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Superpixel Sampling Networks](https://mlsanity.com/paper/1807.10174/) (2018)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Superpixels: An Evaluation of the State-of-the-Art](https://mlsanity.com/paper/1612.01601/) (2016)
- [Superpixel Hierarchy](https://mlsanity.com/paper/1605.06325/) (2016)
- [3D Scene Geometry Estimation from 360^circ Imagery: A Survey](https://mlsanity.com/paper/2401.09252/) (2024)
- [Superpixel Segmentation Using Gaussian Mixture Model](https://mlsanity.com/paper/1612.08792/) (2016)
- [Robust superpixels using color and contour features along linear path](https://mlsanity.com/paper/1903.07193/) (2019)
- [Evaluation Framework of Superpixel Methods with a Global Regularity Measure](https://mlsanity.com/paper/1903.07162/) (2019)
- [A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling](https://mlsanity.com/paper/1505.06236/) (2015)
- [SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches](https://mlsanity.com/paper/1903.07169/) (2019)
- [Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph](https://mlsanity.com/paper/1606.03871/) (2016)
- [Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images](https://mlsanity.com/paper/2004.07394/) (2020)

## Abstract (arXiv)

The growing use of wide angle image capture devices and the need for fast and accurate image analysis in computer visions have enforced the need for dedicated under-representation approaches. Most recent decomposition methods segment an image into a small number of irregular homogeneous regions, called superpixels. Nevertheless, these approaches are generally designed to segment standard 2D planar images, i.e., captured with a 90o angle view without distortion. In this work, we introduce a new general superpixel method called SphSPS (for Spherical Shortest Path-based Superpixels)1 , dedicated to wide 360o spherical or omnidirectional images. Our method respects the geometry of the 3D spherical acquisition space and generalizes the notion of shortest path between a pixel and a superpixel center, to fastly extract relevant clustering features. We demonstrate that considering the geometry of the acquisition space to compute the shortest path enables to jointly improve the segmentation accuracy and the shape regularity of superpixels. To evaluate this regularity aspect, we also generalize a global regularity metric to the spherical space, addressing the limitations of the only existing spherical compactness measure. Finally, the proposed SphSPS method is validated on the reference 360o spherical panorama segmentation dataset and on synthetic road omnidirectional images. Our method significantly outperforms both planar and spherical state-of-the-art approaches in terms of segmentation accuracy,robustness to noise and regularity, providing a very interesting tool for superpixel-based applications on 360o images.