How to Use Wikitude to Automatically Recognize 3D Models

In this article we will look at how to use Wikitude to detect 3D models and collect images of them. The image targets are taken from 3D/CAD software. This means you can use Wikitude to recognize objects from a distance of 312cm or more. However, if you want to use Wikitude to automatically recognize images from 3D models, we will discuss the best ways to use it.

SDK 8.5 image targets can be recognized from 312cm away

The SDK 8.5 update brings significant performance improvements to Wikitude's Image Recognition. Wikitude claims that SDK 8.5 can recognize image targets from 312cm away, a 40% improvement over the previous SDK version. The SDK also offers faster recognition of duplicate targets and significantly improved tracking stability. As a result, developers can create a 3D view of products and scenes using Wikitude's image recognition tracking feature. Non-subscribers can download a free trial version to test the SDK. The SDK makes it easy for developers to create AR experiences that incorporate image-based tracking and recognition. For example, using Multiple Image Recognition and Tracking allows developers to recognize several images simultaneously, whether they're static or interactive. Besides, developers can easily adjust the distance between the image targets and the AR scene. The SDK also makes it possible to adjust the transformation and distance of the image targets.

SDK 8.5 cloud recognition service

You can use the Wikitude SDK to call the Cloud Recognition servers to use the service. In the free version, you can use one target per model; however, if you want to use the full service, you must purchase a commercial license. The commercial license contains 1 million scans per month per developer account. You can use the free trial of the REST API to check the results of your application. However, if you need to use the service in production, you should purchase the commercial license. To use the Cloud Recognition SDK, you need to open your project and create the required attributes. Depending on your requirements, you may need to update the application if the asset is offline. Once you have created the model, you should use the Cloud Recognition SDK to get the data you need. The SDK comes with a sample application that demonstrates how to use the service.

Felgo

The Wikitude SDK is an extension to the Wikitude AR app development framework, allowing developers to build robust AR experiences and mobile apps for Android, iOS, and other platforms. The SDK supports JavaScript, Unity, Flutter, and Native API. This version of the SDK provides significant performance improvements for cylinder targets and supports the creation of augmented videos and 3D models from source material. Here are the advantages of SDK 9.6 for developers: Wikitude's Target Manager lets you add target height information to images and save them as part of an Image Target Collection. After you have created Image Targets, you can easily share them on the cloud, and export projects for embedding in apps. You can use the Studio Editor to visualize and edit the targets you've created. This allows you to preview and publish your augmented reality experience with minimal coding knowledge.

Wikitude

The SDK for Wikitude can use two different methods for creating Object Targets. You can use Wikitude Studio to add target height information to images and automatically apply it to your 3D model. Alternatively, you can use the Target Collection Editor, included with the Wikitude SDK, to create physical targets and store them in the Object Target Collection. This way, you can use multiple Object Targets on one model and apply them in different locations on the same model. The SDK for Wikitude allows you to load 3D models into the augmented reality scene. It supports Blender and Autodesk Maya 3D, and its Cloud Recognition service allows you to work with thousands of target images hosted on the cloud. The SDK for Wikitude is scalable and has a high recognition rate. This enables you to build AR apps for mobile devices, wearables, and websites.

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