Systems and methods utilizing machine vision and three-dimensional modeling techniques for surface matching

A system comprising:

• a three-dimensional (3D) imager configured to capture and provide 3D images of a field of view of the 3D imager, the 3D images including one or more occurrence candidates of one or more objects present in a scene;
• and
• a processor and computer-readable media storage having machine readable instructions stored thereon that, when the machine readable instructions are executed, cause the system to:
• obtain a first 3D image of the field of view of the 3D imager;
• rank the one or more occurrence candidates;
• determine, based on a first metric, a set of occurrence candidates from the ranked one or more occurrence candidates;
• group occurrence candidates of the determined set of occurrence candidates based on at least one attribute of each occurrence candidate;
• determine a confidence level of each group of occurrence candidates based on a number of occurrence candidates in each group;
• select, based on a second metric, an occurrence candidate having a highest second metric value from each group of occurrence candidates;
• and
• perform matching of the selected occurrence candidates with a surface of a 3D model.

One or more tangible non-transitory computer-readable media storing computer-executable instructions that, when executed via one or more processors, cause one or more imaging systems to:

• obtain, via a three-dimensional (3D) camera, a 3D image of a field of view of the 3D camera, the 3D image including one or more occurrence candidates of one or more objects present in a scene;
• rank, via a processor, the one or more occurrence candidates;
• determine, via the processor and based on a first metric, a set of occurrence candidates from the ranked one or more occurrence candidates;
• group, via the processor, occurrence candidates of the determined set of occurrence candidates based on at least one attribute of each occurrence candidate;
• determine, via the processor, a confidence level of each group of occurrence candidates based on a number of occurrence candidates in each group;
• select, via the processor and based on a second metric, an occurrence candidate having a highest second metric value from each group of occurrence candidates;
• and
• perform, via the processor, matching of the selected occurrence candidates with a surface of a 3D model.