Systems and methods for rendering a portion of a 3D display

1. A system comprising:

one or more processors; and

one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors causing the one or more processors to perform operations comprising:

generating a mask of an object using one or more images;

generating a 3D model of the object using the mask of the object;

simulating an artificial 3D capture environment for the 3D model of the object;

generating an artificial surface for the 3D model of the object in the artificial 3D capture environment;

transferring the artificial surface for the 3D model of the object to the one or more images; and

blending the artificial surface for the 3D model of the object with a real-world surface in the one or more images to create a blended surface that (1) comprises both the artificial surface and the real-world surface and (2) has a higher cleanliness level than the real world surface when displayed on an electronic device.

2. The system of claim 1 , wherein:

the simulating the artificial 3D capture environment comprises:

creating a 3D image map of a real-world capture environment where the one or more images were taken; and

performing path tracing in the artificial 3D capture environment using the 3D image map to generate artificial light for the 3D environment; and

the generating the artificial surface for the object comprises:

generating the artificial surface in the 3D capture environment using the artificial light for the 3D environment.

3. The system of claim 1 , wherein the generating the mask of the object using the one or more images comprises:

training a machine learning algorithm on one or more training images;

identifying, using the machine learning algorithm, as trained, (1) the object in the one or more images and (2) objects other than the object in the one or more images;

removing the objects from the one or more images; and

after removing the objects from the one or more images, generating the mask from only the object in the one or more images.

4. The system of claim 3 ,

wherein the machine learning algorithm, as trained, comprises a neural network configured identify an automobile;

wherein the one or more training images comprise one or more voxel models; and

wherein at least a portion of the one or more training images are labeled with:

a make of the automobile;

a model of the automobile; and

a body type of the automobile.

5. The system of claim 1 ,

wherein the mask comprises at least a portion of the one or more images, and

wherein the one or more images comprise images taken radially around the object; and

the generating the 3D model of the object using the mask of the object comprises performing volume carving using at least the portion of the one or more images to create a voxelized model of the object.

6. The system of claim 1 , wherein the generating the artificial surface for the object in the artificial 3D capture environment comprises:

simulating reflections and shadows for the object in the artificial 3D capture environment by reflecting artificial light for the artificial 3D capture environment off of the 3D model of the object;

rendering the artificial surface for the object using path trace rendering at a lower resolution than a display resolution; and

supersampling the reflections and shadows at the display resolution; and

wherein the artificial light is generated using a 3D image map of a real-world capture environment where the one or more images were taken.

7. The system of claim 1 , further comprising:

facilitating displaying a 3D display of the object using the one or more images, the 3D display comprising the artificial surface for the object and the real-world surface for the object.

8. The system of claim 7 , further comprising:

receiving a zoom selection point selected by a user on the 3D display of the object;

facilitating zooming the 3D display of the object into a zoomed 3D display of the object; and

facilitating rotating the zoomed 3D display of the object around the zoom selection point.

9. The system of claim 1 , wherein the artificial 3D capture environment comprises a 3D rendering of a real-world capture environment where the one or more images were taken.

10. The system of claim 1 , wherein the generating the mask of the object comprises:

receiving an image of the object;

determining a respective pixel intensity for each respective pixel in the image; and

when the respective pixel intensity of a respective pixel of each respective pixel is above a predetermined threshold, labeling the respective pixel as not the object.

11. A method comprising:

generating a mask of an object using one or more images;

generating a 3D model of the object using the mask of the object;

simulating an artificial 3D capture environment for the 3D model of the object;

generating an artificial surface for the 3D model of the object in the artificial 3D capture environment;

transferring the artificial surface for the 3D model of the object to the one or more images; and

blending the artificial surface for the 3D model of the object with a real-world surface in the one or more images to create a blended surface that (1) comprises both the artificial surface and the real-world surface and (2) has a higher cleanliness level than the real world surface when displayed on an electronic device.

12. The method of claim 11 , wherein:

the simulating the artificial 3D capture environment comprises:

creating a 3D image map of a real-world capture environment where the one or more images were taken; and

performing path tracing in the artificial 3D capture environment using the 3D image map to generate artificial light for the 3D environment; and

the generating the artificial surface for the object comprises:

generating the artificial surface in the 3D capture environment using the artificial light for the 3D environment.

13. The method of claim 11 , wherein the generating the mask of the object using the one or more images comprises:

training a machine learning algorithm on one or more training images;

identifying, using the machine learning algorithm, as trained, (1) the object in the one or more images and (2) objects other than the object in the one or more images;

removing the objects from the one or more images; and

after removing the objects from the one or more images, generating the mask from only the object in the one or more images.

14. The method of claim 13 ,

wherein the machine learning algorithm, as trained, comprises a neural network configured identify an automobile;

wherein the one or more training images comprise one or more voxel models; and

wherein at least a portion of the one or more training images are labeled with:

a make of the automobile;

a model of the automobile; and

a body type of the automobile.

15. The method of claim 11 ,

wherein the mask comprises at least a portion of the one or more images, and

wherein the one or more images comprise images taken radially around the object; and

the generating the 3D model of the object using the mask of the object comprises performing volume carving using at least the portion of the one or more images to create a voxelized model of the object.

16. The method of claim 11 , wherein the generating the artificial surface for the object in the artificial 3D capture environment comprises:

simulating reflections and shadows for the object in the artificial 3D capture environment by reflecting artificial light for the artificial 3D capture environment off of the 3D model of the object;

rendering the artificial surface for the object using path trace rendering at a lower resolution than a display resolution; and

supersampling the reflections and shadows at the display resolution; and

wherein the artificial light is generated using a 3D image map of a real-world capture environment where the one or more images were taken.

17. The method of claim 11 further comprising:

facilitating displaying a 3D display of the object using the one or more images, the 3D display comprising the artificial surface for the object and the real-world surface for the object.

18. The method of claim 17 , further comprising:

receiving a zoom selection point selected by a user on the 3D display of the object;

facilitating zooming the 3D display of the object into a zoomed 3D display of the object; and

facilitating rotating the zoomed 3D display of the object around the zoom selection point.

19. The method of claim 11 , wherein the artificial 3D capture environment comprises a 3D rendering of a real-world capture environment where the one or more images were taken.

20. The method of claim 11 , wherein the generating the mask of the object comprises:

receiving an image of the object;

determining a respective pixel intensity for each respective pixel in the image; and

in response to the respective pixel intensity of a respective pixel of each respective pixel being above a predetermined threshold, labeling the respective pixel as not the object.