# Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images

arXiv: https://arxiv.org/abs/2104.10900 · HTML: https://mlsanity.com/paper/2104.10900/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments](https://mlsanity.com/paper/1712.03931/) (2017)
- [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019)
- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [Quaternion Based Camera Pose Estimation From Matched Feature Points](https://mlsanity.com/paper/1704.02672/) (2017)

## Built on by (most cited first, 3 of 3)

- [EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching](https://mlsanity.com/paper/2502.20685/) (2025)
- [IM360: Large-scale Indoor Mapping with 360 Cameras](https://mlsanity.com/paper/2502.12545/) (2025)
- [SCCM: Spherically Consistent Coarse Matching for ERP Dense Feature Correspondence](https://mlsanity.com/paper/2609.36545/) (2026)

## Abstract (arXiv)

This paper presents a novel preconditioning strategy for the classic 8-point algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e., equirectangular images) in spherical projection. To alleviate the effect of uneven key-feature distributions and outlier correspondences, which can potentially decrease the accuracy of an essential matrix, our method optimizes a non-rigid transformation to deform a spherical camera into a new spatial domain, defining a new constraint and a more robust and accurate solution for an essential matrix. Through several experiments using random synthetic points, 360-FoV, and fish-eye images, we demonstrate that our normalization can increase the camera pose accuracy by about 20% without significantly overhead the computation time. In addition, we present further benefits of our method through both a constant weighted least-square optimization that improves further the well known Gold Standard Method (GSM) (i.e., the non-linear optimization by using epipolar errors); and a relaxation of the number of RANSAC iterations, both showing that our normalization outcomes a more reliable, robust, and accurate solution.