It is now easier to see how localization results line up with the physical environment. EasyAR Mega Toolbox has enhanced its existing localization testing experience with a model overlay directly on the structural surfaces of the real scene, making results and status more intuitive to inspect on site.
A model overlay gives localization a visible reference
During testing, a model is overlaid directly on walls, floors, ceilings and other physical structures. Through the camera view, users can observe how the model lines up with the environment, giving the localization result a visible reference in the actual space.
The demonstration shows this real-world overlay using a blue-and-gold mesh. As the viewpoint changes, users can inspect the relationship between the model and the physical structures from different positions, alongside the status information in the interface.
View the overlay without building your own app
Toolbox already supported localization testing. Previously, users who wanted to inspect results with this kind of model overlay had to build their own app and take it on site. This update brings that visualization directly into Toolbox, making the existing testing experience clearer and more convenient.
Developers and project teams can inspect the model against physical structures in the target environment to inform subsequent development, adjustments and deployment.
Supported in the mobile app and WeChat Mini Program
The model overlay is available in both the mobile Toolbox app and the WeChat Mini Program Toolbox. Users can choose either entry point to view the overlay and inspect localization status on site.
Requirement: Block build version 9.6 or later
The model overlay requires Block build version 9.6 or later. Older Blocks can be upgraded to build version 9.6 before using this feature.
With a compatible Block ready, users can open Toolbox and observe the model overlaid on the real scene's structures.
Open EasyAR Mega Toolbox and see localization more clearly with a model overlaid on the real scene.
Team EasyAR
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