This research investigates a framework for the efficient development of vision-based dense three-dimensional displacement measurement algorithms to support reliable structural health monitoring of civil structures. The framework exploits the use of a photo-realistic synthetic model, termed a physics-based graphics model, to simulate the entire process of vision-based measurement. At the same time, the synthetic environment is used to evaluate the performance of different post-processing algorithms quantitatively for a given measurement scenario, such as camera selection and camera placement. The effectiveness of the framework is demonstrated by optimizing the algorithms for the three-dimensional displacement measurement of a 14-bay laboratory truss structure. The vision-based dense three-dimensional displacement estimation algorithms optimized in this study consist of four steps: (1) camera parameter estimation, (2) camera motion estimation and compensation, (3) vision-based two-dimensional tracking, and (4) projection of two-dimensional tracking results to three-dimensional space. The algorithms use the knowledge from the finite element model to facilitate the implementation and maximize the measurement outcome, that is, model-informed approach. To test and evaluate the model-informed approach, synthetic videos are rendered for two measurement scenarios, that is, using a Digital Single Lens Reflex camera mounted on a tripod and using an Unmanned Aerial Vehicle camera. Then, the performance of the model-informed approach is evaluated by comparing the estimated displacement with the ground truth values. Based on the performance evaluation, an algorithm with the highest expected performance is selected for each of the two measurement scenarios. Finally, the selected algorithm is tested in a laboratory experiment. In contrast to the existing literature that investigates fixed individual measurement scenarios, the proposed framework can be used to test different measurement scenarios and estimate the outcome of each scenario before performing actual tests, facilitating the implementation of vision-based measurement for the structural health monitoring of civil structures.