The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a rail-driven high-throughput plant phenotyping platform (HTPPP) in field conditions. An adaptive sliding window segmentation algorithm was proposed to obtain plots and rows from canopy point clouds. Maximum height (H_max), mean height (H_mean), and canopy cover (CC) of each plot were extracted, and quantification of plot canopy height uniformity (CHU) and marginal effect (ME_H) was achieved. The results showed that the average mIoU, mP, mR, and mF₁ of canopy–plot segmentation were 0.8118, 0.9587, 0.9969, and 0.9771, respectively, and the average mIoU, mP, mR, and mF₁ of plot–row segmentation were 0.7566, 0.8764, 0.9292, and 0.8974, respectively. The average RMSE of plant height across the 10 growth stages was 0.08 m. The extracted time-series phenotypes show that CHU tended to vary from uniformity to nonuniformity and continued to fluctuate during the whole growth stages, and the ME_H of the canopy tended to increase negatively over time. This study provides automated and practical means for 3D time-series phenotype monitoring of plant canopies with the HTPPP.