Scene understanding and 3D imagination: A comparison between machine learning and human cognition

Spatial perception and three-dimensional imagination are important characteristics for many construction tasks in civil engineering. In order to support people in these tasks, worldwide research is being carried out on assistance systems based on machine learning and augmented reality. In this paper, we examine the machine learning component and compare it to human performance. The test scenario is to recognize a partly-assembled model, identify its current status, i.e. the current instruction step, and to return the next step. Thus, we created a database of 2D images containing the complete set of instruction steps of the corresponding 3D model. Afterwards, we trained the deep neural network RotationNet with these images. Usually, the machine learning approaches are compared to each other; our contribution evaluates the machine learning results with human performance tested in a survey: in a clean-room setting the survey and RotationNet results are comparable and neith er is significantly better. The real-world results show that the machine learning approaches need further improvements.