An Automated Grading/Feedback System for 3-View Engineering Drawings using RANSAC

Abstract

We propose a novel automated grading system that can compare two multiview engineering drawings consisting of three views that may have allowable translations, scales, and offsets, and can recognize frequent error types as well as individual drawing errors. We show that translation, scale, and offset-invariant comparison can be conducted by estimating the affine transformation for each individual view within drawings. Our system directly aims to evaluate students' skills creating multiview engineering drawings. Since it is important for our students to be familiar with widely used software such as AutoCAD, our system does not require a separate interface or environment, but directly grades the saved DWG/DXF files from AutoCAD. We show the efficacy of the proposed algorithm by comparing its results with human grading. Beyond the advantages of convenience and accuracy, based on our data set of students' answers, we can analyze the common errors of the class as a whole using our system.