This work questions the role of precision and fidelity in our experience and representation of a “real” physical environment. On the one hand, the resulting VR environment is less complete and has lower fidelity than digital environments created through traditional modeling and rendering workflows. On the other hand, because each point in the point cloud is literally sampled from the actual environment, the resulting model also captures more of the noise and imprecision that characterizes our world. The result is an uncanny immersive experience that is less precise than traditional digital environments, yet represents many more of the unique physical characteristics that define our urban experiences.

CityMatrix was introduced to address these challenges. Machine learning techniques were applied to achieve real-time prediction of multiple urban simulations, and thousands of city configurations were simulated. The simulation results were used to train a convolutional neural network (CNN) to predict the traffic and solar performance of unseen city configurations. The prediction with the CNN is thousands of times faster than the original simulations and maintains a high-quality representation of the results. This machine learning approach was applied as a versatile, quick, accurate, and computationally efficient method not only for real-time feedback, but also for optimized design recommendations. Users involved in the evaluation of this project had a better understanding of the embodied trade-offs of the city and achieved their goals in an efficient manner.