The conventional method of measuring foot-arch parameters is highly dependent on the measurer's skill level, so accurate measurements are difficult to obtain. To solve this problem, we propose an autonomous geometric foot-arch analysis platform that is capable of capturing the sole of the foot and yields three foot-arch parameters: arch index (AI), arch width (AW) and arch height (AH). The proposed system captures 3D geometric and color data on the plantar surface of the foot in a static standing pose using a commercial RGB-D camera. It detects the region of the foot surface in contact with the footplate by applying the clustering and Markov random field (MRF)-based image segmentation methods. The system computes the foot-arch parameters by analyzing the 2/3D shape of the contact region. Validation experiments were carried out to assess the accuracy and repeatability of the system. The average errors for AI, AW, and AH estimation on 99 data collected from 11 subjects during 3 days were -0.17%, 0.95 mm, and 0.52 mm, respectively. Reliability and statistical analysis on the estimated foot-arch parameters, the robustness to the change of weights used in the MRF, the processing time were also performed to show the feasibility of the system.