Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation

Tianyu Luan, Zhong Li, Lele Chen, Xuan Gong, Lichang Chen, Yi Xu, Junsong Yuan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20155-20164

Existing 3D mesh shape evaluation metrics mainly focus on the overall shape but are usually less sensitive to local details. This makes them inconsistent with human evaluation as human perception cares about both overall and detailed shape. In this paper we propose an analytic metric named Spectrum Area Under the Curve Difference (SAUCD) that demonstrates better consistency with human evaluation. To compare the difference between two shapes we first transform the 3D mesh to the spectrum domain using the discrete Laplace-Beltrami operator and Fourier transform. Then we calculate the Area Under the Curve (AUC) difference between the two spectrums so that each frequency band that captures either the overall or detailed shape is equitably considered. Taking human sensitivity across frequency bands into account we further extend our metric by learning suitable weights for each frequency band which better aligns with human perception. To measure the performance of SAUCD we build a 3D mesh evaluation dataset called Shape Grading along with manual annotations from more than 800 subjects. By measuring the correlation between our metric and human evaluation we demonstrate that SAUCD is well aligned with human evaluation and outperforms previous 3D mesh metrics.