Training Climbing Roses by Constrained Graph Search

Cultivated climbing roses are skillfully shaped by arranging their stems manually against support walls to enhance their aesthetic appeal. This study introduces a procedural technique designed to replicate the branching pattern of climbing roses, simulating the manual training process. The central idea of the proposed approach is the conceptualization of tree modeling as a constrained path-finding problem. The primary goal is to optimize the stem structure to achieve the most impressive floral display. The proposed method operates iteratively, generating multiple stems while applying the objective function in each iteration for maximizing coverage on the support wall. Our approach offers a diverse range of tree forms employing only a few parameters, which eliminates the requirement for specialized knowledge in cultivation or plant ecology.