Methods and systems for training an object detection algorithm using synthetic images

A non-transitory computer readable medium that embodies instructions that cause one or more processors to perform a method comprising:

• (a) selecting a 3D model corresponding to an object;
• (b) generating domain-adapted images of the 3D model, the images representing the 3D model at corresponding poses, including:
• (b1) providing the 3D model with information representing randomly or algorithmically chosen or generated textures that are different from one another;
• and
• (b2) rendering the 3D model at the corresponding poses to obtain the domain-adapted images, at least two of the domain-adapted images representing the 3D model with the textures that are different between the at least two of the domain-adapted images;
• (c) training an algorithm model to learn correspondences between the acquired images and corresponding classes, the classes representing respective orientations of the 3D model;
• and
• (d) storing, in a memory, parameters representing the algorithm model.

A method comprising:

• (a) using one or more processors, selecting a 3D model corresponding to an object;
• (b) using the one or more processors, generating domain-adapted images of the 3D model, the domain-adapted images representing the 3D model at corresponding poses, including:
• (b1) providing the 3D model with information representing randomly or algorithmically chosen or generated textures that are different from one another;
• and
• (b2) rendering the 3D model at the corresponding poses to obtain the domain-adapted images, at least two of the domain-adapted images representing the 3D model with the textures that are different between the at least two of the domain-adapted images;
• (c) using the one or more processors, acquiring 2D projections of 3D points on a 3D bounding box defined around the 3D model at the corresponding poses;
• (d) using the one or more processors, training an algorithm model to learn correspondences between the generated images and the corresponding 2D projections;
• and
• (e) storing, in a memory, parameters representing the algorithm model.

A method comprising:

• (a) using one or more processors, selecting a 3D model corresponding to an object;
• (b) using the one or more processors, generating domain-adapted images of the 3D model, the images representing the 3D model at corresponding poses, including:
• (b1) providing the 3D model with information representing randomly or algorithmically chosen or generated textures that are different from one another;
• and
• (b2) rendering the 3D model at the corresponding poses to obtain the domain-adapted images, at least two of the domain-adapted images representing the 3D model with the textures that are different between the at least two of the domain-adapted images;
• (c) using the one or more processors, training an algorithm model to learn correspondences between the acquired images and corresponding classes, the classes representing respective orientations of the 3D model;
• and
• (d) storing, in a memory, parameters representing the algorithm model.