Recognizing perspective accuracy

Abstract

Sketching in perspective is a valuable skill for art, and for professional disciplines like industrial design, architecture, and engineering. However, it tends to be a difficult skill to grasp for novices. We have developed an algorithm that can classify rectilinear perspective strokes, as well as classify which of those strokes are accurate on a stroke-by-stroke basis. We also developed an intelligent user interface which can provide real-time accuracy feedback on a user's free-hand digital perspective sketch. To evaluate the system, we conducted a between-subjects user study with 40 novice participants which involved sketching city street corners in 2-point perspective. We discovered that the participants who received real-time intelligent feedback improved their perspective accuracy significantly (p < 0.0005) in a subsequent unassisted sketch, suggesting a mild learning effect and knowledge transfer. Through qualitative feedback we also derived principles for how intelligent real-time feedback can best assist novices in learning perspective.