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Increasingly, urban planners are adopting virtual reality (VR) in designing urban green spaces (UGS) to visualize landscape designs in immersive 3D. However, the psychological effect of green spaces from the experience in VR may differ from the actual experience in the real world. In this paper, we systematically reviewed studies in the literature that conducted experiments to investigate the psychological benefits of nature in both VR and the real world to study nature in VR anchored to nature in the real world. We separated these studies based on the type of VR setup used, specifically, 360-degree video or 3D virtual environment, and established a framework of commonly used standard questionnaires used to measure the perceived mental states. The most common questionnaires include Positive and Negative Affect Schedule (PANAS), Perceived Restorativeness Scale (PRS), and Restoration Outcome Scale (ROS). Although the results from studies that used 360-degree video were less clear, results from studies that used 3D virtual environments provided evidence that virtual nature is comparable to real-world nature and thus showed promise that UGS designs in VR can transfer into real-world designs to yield similar physiological effects.

Estimation of the aerodynamic load on trees is essential for urban tree management to mitigate the risk of tree failure. To assess that in a cost-effective way, scaled down tree models and numerical simulations were utilized. Scaled down tree models reduce the cost of experimental studies and allow the studies to be conducted in a controlled environment, namely in a wind or water tunnel, but the major challenge is to construct a tree model that resembles the real tree. We constructed 3D-printed scaled down fractal tree models of major urban tree species in Singapore using procedural modelling, based on species-specific growth processes and field statistical data gathered through laser scanning of real trees. The tree crowns were modelled to match the optical porosity of real trees. We developed a methodology to model the tree crowns using porous volumes filled with randomized tetrahedral elements. The wind loads acting on the tree models were then measured in the wind tunnel and the velocity profiles from selected models were captured using particle image velocimetry (PIV). The data was then used for the validation of Large Eddy Simulations (LES), in which the trees were modelled via a discretized momentum sink with 10–20 elements in width, height, and depth, respectively. It is observed that the velocity profiles and drag of the simulations and the wind tunnel tests are in reasonable agreement. We hence established a clear relationship between the measured bulk drag on the tree models in the wind tunnel, and the local drag coefficients of the discretized elements in the simulations. Analysis on the bulk drag coefficient also shows that the effect of complex crown shape could be more dominant compared to the frontal optical porosity.

We propose a method that automatically generates a smooth chase camera movement to follow a subject, a user-controlled character or a character with unknown behaviors, in a 3D environment freely in real time. We consider three objectives in generating the smooth-camera movement: to avoid collisions with obstacles, to avoid subject occlusions, and to choose a good viewpoint for looking at the subject. We evaluate the goodness of viewpoints by using a viewpoint entropy map and choose the best viewpoint as the goal position of the camera in real time. Afterwards, we move the camera toward the goal position by following the shortest path, found by the A* algorithm, on a roadmap graph. The resulting camera movement has a high degree of freedom and fulfills the three objectives above. Our method is effective for third-person-view 3D applications in tracking the real-time movement of user-controlled characters in exploring a 3D environment.