Three-dimensional (3D) depth imaging systems and methods for automatically determining shipping container fullness based on imaging templates

1. A three-dimensional (3D) depth imaging system configured to automatically determining shipping container fullness based on imaging templates, the 3D depth imaging system comprising:

a 3D-depth camera configured to capture 3D image data, the 3D-depth camera oriented in a direction to capture 3D image data of a shipping container located in a predefined search space during a shipping container loading session, the shipping container having a shipping container type; and

a container fullness application (app) configured to execute on one or more processors communicatively coupled to the 3D-depth camera, the container fullness app further configured to:

determine, based on 3D image data received from the 3D-depth camera, a 3D container point cloud representative of the shipping container,

load, into a memory communicatively coupled to the one or more processors, an imaging template that defines a 3D template point cloud corresponding to the shipping container type, the 3D template point cloud comprising a 3D template front portion of the shipping container type and a 3D template back portion of the shipping container type,

identify, within the 3D container point cloud, a 3D container front portion corresponding to a physical container front portion of the shipping container,

generate a 3D mapping of the 3D container point cloud and the 3D template point cloud, the 3D mapping comprising the 3D container front portion of the 3D container point cloud aligned with the 3D template front portion of the 3D template point cloud, and

determine, based on the 3D mapping, a fullness value of the shipping container.

2. The 3D depth imaging system of claim 1 , wherein the 3D container point cloud representative of the shipping container is a cropped portion of point cloud data of the 3D image data received from the 3D-depth camera.

3. The 3D depth imaging system of claim 1 , wherein the container fullness application determines the imaging template based on the 3D image data received from the 3D-depth camera, wherein a machine learning model, trained with 3D image data defining a plurality of shipping container types, inputs the 3D image data and outputs the shipping container type, and wherein the imaging template is loaded based on the shipping container type.

4. The 3D depth imaging system of claim 1 , wherein generating the 3D mapping comprises executing a closest point algorithm that generates a 3D match score based on distances between a first set of 3D points of the 3D container point cloud and a second set of 3D points of the 3D template point cloud.

5. The 3D depth imaging system of claim 1 , wherein the imaging template is a non-point cloud imaging template, and wherein in loading the imaging template includes converting, by the one or more processors, the non-point cloud template to the 3D template point cloud.

6. The 3D depth imaging system of claim 5 , wherein the non-point cloud template is a computer aided design (CAD) model.

7. The 3D depth imaging system of claim 1 , wherein the imaging template is preloaded into the memory.

8. The 3D depth imaging system of claim 1 , wherein the shipping container is a unit load device (ULD).

9. The 3D depth imaging system of claim 8 , wherein the shipping container type is a ULD type being one of: an AMJ type, an AAD type, an AKE type, an AYY type, a SAA type, an APE type, or an AQF type.

10. The 3D depth imaging system of claim 1 , wherein the imaging template comprises a template of a unit load device (ULD).

11. The 3D depth imaging system of claim 1 , wherein the 3D-depth camera and the one or more processors are housed in a mountable device.

12. The 3D depth imaging system of claim 1 , wherein the one or more processors are located at a server communicatively coupled to the 3D-depth camera via a digital network.

13. The 3D depth imaging system of claim 1 , wherein determining the fullness value comprises projecting the 3D mapping onto a two-dimensional (2D) grid corresponding to a storage area of the shipping container.

14. The 3D depth imaging system of claim 1 , further comprising a dashboard app, the dashboard app executing on a client device implementing a guided user interface (GUI), the GUI graphically indicating the fullness value of the shipping container.

15. A three-dimensional (3D) depth imaging method for automatically determining shipping container fullness based on imaging templates, the 3D depth imaging method comprising:

determining, based on 3D image data received from a 3D-depth camera, a 3D container point cloud representative of a shipping container, the shipping container located in a predefined search space during a shipping container loading session, the shipping container having a shipping container type;

loading, into a memory communicatively coupled to one or more processors, an imaging template that defines a 3D template point cloud corresponding to the shipping container type, the 3D template point cloud comprising a 3D template front portion of the shipping container type and a 3D template back portion of the shipping container type;

identifying, within the 3D container point cloud, a 3D container front portion corresponding to a physical container front portion of the shipping container;

generating a 3D mapping of the 3D container point cloud and the 3D template point cloud, the 3D mapping comprising the 3D container front portion of the 3D container point cloud aligned with the 3D template front portion of the 3D template point cloud; and

determining, based on the 3D mapping, a fullness value of the shipping container.

16. A tangible, non-transitory computer-readable medium storing instructions for automatically determining shipping container fullness based on imaging templates, that when executed by one or more processors of a computing device cause the one or more processors of the computing device to:

determine, based on 3D image data received from a 3D-depth camera, a 3D container point cloud representative of a shipping container, the shipping container located in a predefined search space during a shipping container loading session, the shipping container having a shipping container type;

load, into a memory communicatively coupled to one or more processors, an imaging template that defines a 3D template point cloud corresponding to the shipping container type, the 3D template point cloud comprising a 3D template front portion of the shipping container type and a 3D template back portion of the shipping container type;

identify, within the 3D container point cloud, a 3D container front portion corresponding to a physical container front portion of the shipping container;

generate a 3D mapping of the 3D container point cloud and the 3D template point cloud, the 3D mapping comprising the 3D container front portion of the 3D container point cloud aligned with the 3D template front portion of the 3D template point cloud; and

determine, based on the 3D mapping, a fullness value of the shipping container.