This paper introduces a method to generate a three-dimensional (3D) virtual model of an imaginary city from a single street-view image to represent the appearance of the city in a given input photograph. The proposed approach differs from reconstruction approaches, which generate a city model by guessing the city name from the input photograph. In contrast, we use machine learning to identify where to generate the city, what to allocate in the city, and how to arrange the components. We employ generative adversarial networks and convolutional neural networks to create a terrain map and identify the components and styles that represent the virtual city appearance. We demonstrate that our system creates 3D virtual cities that are visually similar in terms of plausibility and naturalness to actual cities corresponding to input photographs from around the world. To the best of our knowledge, this is the first work to generate a city model including all general city components, including streets, buildings, and vegetation, to match the style of a single input image.

CityCraft: 3D virtual city creation from a single image

The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or can be learned from satellite images and land register data. Together with approaches for generating buildings and trees, this allows us to populate urban landscapes with complex 3D vegetation. The effectiveness of our framework is shown through examples of large-scale city scenes and close-ups of individually grown tree models; we also validate it by a perceptual user study.

Procedural Urban Forestry.

Although an important component of natural scenes, the representation of skyscapes is often relatively simplistic. This can be largely attributed to the complexity of the thermodynamics underpinning cloud evolution and wind dynamics, which make interactive simulation challenging. We address this problem by introducing a novel layered model that encompasses both terrain and atmosphere, and supports efficient meteorological simulations. The vertical and horizontal layer resolutions can be tuned independently, while maintaining crucial inter-layer thermodynamics, such as convective circulation and land-air transfers of heat and moisture. In addition, we introduce a cloud-form taxonomy for clustering, classifying and upsampling simulation cells to enable visually plausible, finely-sampled volumetric rendering. As our results demonstrate, this pipeline allows interactive simulation followed by up-sampled rendering of extensive skyscapes with dynamic clouds driven by consistent wind patterns. We validate our method by reproducing characteristic phenomena such as diurnal shore breezes, convective cells that contribute to cumulus cloud formation, and orographic effects from moist air driven upslope.

https://hal.archives-ouvertes.fr/hal-02517935/document

Interactive Meso-scale Simulation of Skyscapes

Additionally, One of the greatest challenges to mankind is understanding the underlying principles of climate change. Over the last years, the role of forests in climate change has received increased attention. This is due to the observation that not only the atmosphere has a principal impact on vegetation growth but also that vegetation is contributing to local variations of weather resulting in diverse microclimates. The interconnection of plant ecosystems and weather is described and studied as ecoclimates. In this work we take steps towards simulating ecoclimates by modeling the feedback loops between vegetation, soil, and atmosphere. In contrast to existing methods that only describe the climate at a global scale, our model aims at simulating local variations of climate. Specifically, we model tree growth interactively in response to gradients of water, temperature and light. As a result, we are able to capture a range of ecoclimate phenomena that have not been modeled before, including geomorphic controls, forest edge effects, the Foehn effect and spatial vegetation patterning. To validate the plausibility of our method we conduct a comparative analysis to studies from ecology and climatology. Consequently, our method advances the state-of-the-art of generating highly realistic outdoor landscapes of vegetation.

/pdf/ecoclimates-climate-response-modeling-of-vegetation-2gvyqdd7.pdf

Ecoclimates: climate-response modeling of vegetation

While analysing and synthesising 2D distributions of points has been applied both to the generation of textures with discrete elements and for populating virtual worlds with 3D objects, the results are often inaccurate since the spatial extent of objects cannot be expressed. We introduce three improvements enabling the synthesis of more general distributions of elements. First, we extend continuous pair correlation function (PCF) algorithms to multi-class distributions using a dependency graph, thereby capturing interrelationships between distinct categories of objects. Second, we introduce a new normalised metric for disks, which makes the method applicable to both point and possibly overlapping disk distributions. The metric is specifically designed to distinguish perceptually salient features, such as disjoint, tangent, overlapping, or nested disks. Finally, we pay particular attention to convergence of the mean PCF as well as the validity of individual PCFs, by taking into consideration the variance of the input. Our results demonstrate that this framework can capture and reproduce real-life distributions of elements representing a variety of complex semi-structured patterns, from the interaction between trees and the understorey in a forest to droplets of water. More generally, it applies to any category of 2D object whose shape is better represented by bounding circles than points.

/pdf/accurate-synthesis-of-multi-class-disk-distributions-9pjlvmvit8.pdf

Accurate Synthesis of Multi‐Class Disk Distributions

One challenge in portraying large-scale natural scenes in virtual environments is specifying the attributes of plants, such as species, size and placement, in a way that respects the features of natural ecosystems, while remaining computationally tractable and allowing user design To address this, we combine ecosystem simulation with a distribution analysis of the resulting plant attributes to create biome-specific databases, indexed by terrain conditions, such as temperature, rainfall, sunlight and slope For a specific terrain, interpolated entries are drawn from this database and used to interactively synthesize a full ecosystem, while retaining the fidelity of the original simulations A painting interface supplies users with semantic brushes for locally adjusting ecosystem age, plant density and variability, as well as optionally picking from a palette of precomputed distributions Since these brushes are keyed to the underlying terrain properties a balance between user control and real-world consistency is maintained Our system can be be used to interactively design ecosystems up to 5 × 5 km2 in extent, or to automatically generate even larger ecosystems in a fraction of the time of a full simulation, while demonstrating known properties from plant ecology such as succession, self-thinning, and underbrush, across a variety of biomes

/pdf/ecobrush-interactive-control-of-visually-consistent-large-1o8hhh4nqu.pdf

EcoBrush: Interactive Control of Visually Consistent Large-Scale Ecosystems

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EcoBrush: Interactive Control of Visually Consistent Large-Scale Ecosystems