###### Abstract  
In this paper, we address the challenge of estimating the 6 DoF pose of objects in 2D equirectangular images. This solution allows the transition to the objects' 3D model from their current pose. In particular, it finds application in the educational use of 360 deg videos, where it enhances the learning experience of students by making it more engaging and immersive due to the possible interaction with 3D virtual models. We developed a general approach usable for any object and shape. The only requirement is to have an accurate CAD model, even without textures of the item, whose pose must be estimated. The developed pipeline has two main steps: vehicle segmentation from the image background and estimation of the vehicle pose. To accomplish the first task, we used deep learning methods, while for the second, we developed a 360 deg camera simulator in Unity to generate synthetic equirectangular images used for comparison. We conducted our tests using a miniature truck model whose CAD was at our disposal. The developed algorithm was tested using a metrological analysis applied to real data. The results showed a mean difference of 1.5 deg with a standard deviation of 1 deg from the ground truth data for rotations, and 1.4 cm with a standard deviation of 1.5 cm for translations over a research range of \(\pm\)20 deg and \(\pm\)20 cm, respectively.  
image processing: 6 DoF pose estimation; mixed reality; human empowerment; educational setting +
Footnote †: journal: _aciences_  
_Applied_  
_Acticle_  
**Citation:** [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Object Pose Detection to Enable 3D Interaction from 2D Equirectangular Images in Mixed Reality Educational Settings.  
_Applid_  
Received: 20 April 2022  
Accepted: 23 May 2022  
Published: 24 May 2022  
**Published:**  
**Published:**  
From a technological point of view, it is possible to use immersive solutions that exploit VR gears that are able to display virtual environments augmented with real content, a real environment fully reconstructed in 3D or, enabled by a modern high-resolution whole field of view cameras--360\({}^{\circ}\) videos. A great effort is needed to accurately replicate real 3D environments in virtual scenarios: acquiring 360\({}^{\circ}\) videos is a much simpler process. On the other hand, 360\({}^{\circ}\) videos are not able to provide 3D perception.  
For this reason, we propose a method to generate 2D to 3D transitions starting from 360\({}^{\circ}\) source equirectangular images: when an interaction with an object is needed, we can propose to the user the 3D virtual model of the latter. To accomplish this, there is the need to develop a robust algorithm capable of estimating the object's pose inside the 360\({}^{\circ}\) frame. 6 DoF estimation is one of the main challenging research topics in computer vision [12; 13; 14].  
Specifically, in this study we present a novel pipeline whose aim is to infer the pose of a vehicle given a single 2D equirectangular image. The proposed algorithms are described in Section 5, and the experimental results are provided in Section 6. Although the exploitation is very general, we furthermore demonstrate the application of our approach. Therefore, in Section 2 we show the effects of human neuropsychological empowerment through MR, and we provide an example implementation in Section 3, which demonstrates the proposed pipeline for enabling 2D to 3D transitions in MR environments.