Matching visually observed 3D model to original reference object

Abstract

In the development of a visual environment simulator or a thermal imager simulator, one of the challenges encountered when assessing the external appearance of engineering structures is the need to ensure the correspondence of the developed 3D model of a reference object to the original 3D object. Since creating a complete model is currently impractical, the criterion for assessment is based on the conformity of forming specified components of the cognitive model of a human observer necessary in their professional activities. Research has shown that to address this challenge, it is advisable to choose one of the artificial intelligence methods to entrust it with the task of evaluating the developed 3D model and minimize the subjectivity in the decision-making process of “whether the external appearance of the 3D model meets the client's requirements.” The article proposes using deductive reasoning in ChatGPT to assess the conformity of the external appearance of a 3D model to a specified 3D object by representing the model as incomplete sequences and evaluating its ability to complete them in accordance with conclusions made by humans.The suggested approach involves solving the problem based on scenarios where humans formulate logical conclusions from provided information, followed by the application of ChatGPT in processing the presented sequences of conclusions. A comparative analysis is presented to determine the extent to which ChatGPT demonstrates deductive reasoning and how closely it aligns with human deductive models. This allows for the creation of a model with deductive reasoning and contributes to discussions on the competencies of artificial intelligence.