Systems and methods for enabling precise object interaction within an augmented reality environment
The present disclosure provides systems and methods for displaying a real-world vehicle in an augmented reality environment. The system employs a user device camera to obtain image data of an environment that includes a real-world vehicle. The system analyzes the image data to identify the vehicle within the environment. A wireframe model of the vehicle is then generated and registered to the vehicle. The image data is displayed on the user device. In response to user input, the system may then attach a virtual vehicle accessory to the wireframe model. The accessory is then displayed on the user device display in an augmented reality environment such that the vehicle appears to seamlessly incorporate the accessory.
1. A computer-implemented method for displaying a real-world vehicle in an augmented reality environment, the method comprising:
using a camera of a user device to obtain image data of an environment having a real-world vehicle therein;
displaying, on a display of the user device, instructions to scan an accessory of the real-world vehicle;
analyzing the image data to identify the real-world accessory within the environment;
displaying, on the display, a virtual accessory;
displaying, on the display, instructions to align the virtual accessory with the real-world vehicle accessory;
after the virtual image is aligned with the real-world vehicle accessory on the display, generating a wireframe model of the virtual accessory;
registering the wireframe model to the real-world vehicle accessory;
attaching a virtual accessory to the wireframe model; and
displaying the virtual accessory on the display in an augmented reality environment such that the vehicle appears to incorporate the virtual accessory.
2. The computer-implemented method as defined in claim 1 , wherein the wireframe model is a three-dimensional spatial model of the vehicle.
3. The computer-implemented method as defined in claim 1 , wherein a trained machine learning model is used to identify the vehicle within the environment.
4. The computer-implemented method as defined in claim 3 , wherein the machine learning model is trained using training images of multiple vehicle characteristics.
5. The computer-implemented method as defined in claim 3 , wherein the machine learning model is used to map features of the vehicle to the wireframe model.
6. The computer-implemented method as defined in claim 5 , wherein the machine learning model is used to attach the virtual accessory to a corresponding portion of the wireframe model, thereby resulting in display of the virtual accessory on the user device display such that the vehicle appears to incorporate the virtual accessory.
7. The computer-implemented method as defined in claim 1 , wherein the image data is a still image, a live video stream or a recorded video of the environment.
8. A system for displaying a real-world vehicle in an augmented reality environment, the system comprising:
a processor being operable to perform operations comprising:
using a camera of a user device to obtain image data of an environment having a real-world vehicle therein;
displaying, on a display of the user device, instructions to scan an accessory of the real-world vehicle;
analyzing the image data to identify the real-world accessory within the environment;
displaying, on the display, a virtual accessory;
displaying, on the display, instructions to align the virtual accessory with the real-world vehicle accessory;
after the virtual image is aligned with the real-world vehicle accessory on the display, generating a wireframe model of the virtual accessory;
registering the wireframe model to the real-world vehicle accessory;
attaching a virtual accessory to the wireframe model; and
displaying the virtual accessory on the display in an augmented reality environment such that the vehicle appears to incorporate the virtual accessory.
9. The system as defined in claim 8 , wherein the wireframe model is a three-dimensional spatial model of the vehicle.
10. The system as defined in claim 8 , wherein a trained machine learning model is used to identify the vehicle within the environment.
11. The system as defined in claim 10 , wherein the machine learning model is trained using training images of multiple vehicle characteristics.
12. The system as defined in claim 10 , wherein the machine learning model is used to map features of the vehicle to the wireframe model.
13. The system as defined in claim 12 , wherein the machine learning model is used to attach the virtual accessory to a corresponding portion of the wireframe model, thereby resulting in display of the accessory on the user device display such that the vehicle appears to incorporate the virtual accessory.
14. The system as defined in claim 8 , wherein the image data is a still image, a live video stream or a recorded video of the environment.