A method is presented for perceptually characterizing appearance non-uniformities that result from 3D printing. In contrast to physical measurements, the model is designed to take into account the human visual system and variations in observer conditions such as lighting, point of view, and shape. Additionally, it is capable of handling spatial reectance variations over a material’s surface. Motivated by Schrödinger’s line element approach to studying color dierences, an image-based psychophysical experiment that explores paths between materials in appearance space is conducted. The line element concept is extended from color to spatially-varying appearances–including color, roughness and gloss-which enables the measurement of ne dierences between appearances along a path. We dene two path functions, one interpolating reectance parameters and the other interpolating the nal imagery. An image-based uniformity model is developed, applying a trained neural network to color dierences calculated from rendered images of the printed non-uniformities. The nal model is shown to perform better than commonly used image comparison algorithms, including spatial pattern classes that were not used in training.