Evaluating the Perceptual Space of Surface Noise for Visualization

Surface noise provides a geometric alternative to color for encoding scalar information on surfaces, yet its perceptual characteristics remain insufficiently understood. We present a systematic investigation of how variations in noise amplitude and frequency are perceived when applied to 3D surfaces. Across three online perceptual studies with 142 participants, we gathered similarity judgments for surface noise stimuli and modeled the resulting perceptual space using multi‐dimensional scaling (MDS). Our analysis shows that the amplitude–frequency perceptual space requires at least three dimensions for accurate reconstruction; however, the stimuli lie near a 2D manifold. Building on these findings, we derive locally perceptually uniform reparameterizations for both amplitude and frequency, improving the suitability of surface noise as a mapping for scalar data. These results provide perceptual guidance for the design of geometric encodings based on surface perturbation.