Augmented Reality (AR) applications developers and end-users face a dilemma.
On one hand, major IT actors have released toolkits to develop AR applications. Nevertheless, they do not always meet the specific needs required by dedicated use cases or contextual environments (localization accuracy, lighting conditions, indoor/outdoor environments, tracking area range, dynamic scenes, etc.).
No solution fits all, and generally, these toolkits do not provide the level of tuning required to optimally adapt the vision based localization solution to the use case. Moreover, these closed solutions do not always ensure the confidentiality of data, and can store information concerning your environment (3D maps, key frames) or the augmentations (3D meshes, procedure scenarios) that could contain crucial intellectual property and private information.
On the other hand, open source vision libraries and SLAM implementations can generally be modified and configured to optimally meet AR applications requirements. However, many SLAM implementations generally developed by academic actors do not provide the license or the level of maturity required for the development of commercial solutions. Likewise, open source vision libraries offer a huge number of low-level functions but require a huge expertise and important development resources to obtain a usable camera pose estimation pipeline ready for commercial use.
To that end, SolAR offers an alternative to current commercial AR SDKs or existing open-source solutions, providing the benefits of both worlds – openness, ease of use, efficiency, adaptiveness. It aims at creating an ecosystem bringing researchers, developers, and end-users together to help the adoption of augmented reality.