Classification of three-dimensional (3D) objects in a virtual environment

A non-transitory computer-readable medium comprising instructions that, responsive to execution by a processing device, cause the processing device to perform operations comprising:

• generating a plurality of images of a candidate 3D object, wherein each image of the plurality of images of the candidate 3D object is from a respective camera position of two or more camera positions;
• determining one or more histogram of oriented gradients (HOG) vectors for each image of the plurality of images of the candidate 3D object;
• determining an asset feature of the candidate 3D object based on the one or more HOG vectors for each of the plurality of images of the candidate 3D object;
• determining if the asset feature of the candidate 3D object matches an authentic asset feature of at least one authentic 3D object by calculating a vector distance between the asset feature of the candidate 3D object and the authentic asset feature of the at least one authentic 3D object, wherein it is determined that the asset feature of the candidate 3D object matches the authentic asset feature of the at least one authentic 3D object if the vector distance meets a threshold vector distance;
• if the asset feature of the candidate 3D object matches the authentic asset feature of the at least one authentic 3D object, classifying the candidate 3D object as an inauthentic object;
• and
• if the asset feature of the candidate 3D object does not match the authentic asset feature of the at least one authentic 3D object, classifying the candidate 3D object as an authentic object.

A system comprising:

• a memory with instructions stored thereon;
• and
• a processing device, coupled to the memory, the processing device configured to access the memory and execute the instructions, wherein the instructions cause the processing device to perform operations including:
• generating a plurality of images of a candidate 3D object, wherein each image of the plurality of images of the candidate 3D object is from a respective camera position of two or more camera positions;
• determining one or more histogram of oriented gradients (HOG) vectors for each image of the plurality of images of the candidate 3D object;
• determining an asset feature of the candidate 3D object based on the one or more HOG vectors for each of the plurality of images of the candidate 3D object;
• determining if the asset feature of the candidate 3D object matches an authentic asset feature of at least one authentic 3D object by calculating a vector distance between the asset feature of the candidate 3D object and the authentic asset feature of the at least one authentic 3D object, wherein it is determined that the asset feature of the candidate 3D object matches the authentic asset feature of the at least one authentic 3D object if the vector distance meets a threshold vector distance;
• if the asset feature of the candidate 3D object matches the authentic asset feature of the at least one authentic 3D object, classifying the candidate 3D object as an inauthentic object;
• and
• if the asset feature of the candidate 3D object does not match the authentic asset feature of the at least one authentic 3D object, classifying the candidate 3D object as an authentic object.