In this paper we present a framework for the rendering of dynamic 3D virtual environments which can be integrated in the development of videogames. It includes methods to manage sounds and particle effects, paged static geometries, the support of a physics engine and various input systems. It has been designed with a modular structure to allow future expansions. We exploited some open-source state-of-the-art components such as OGRE, PhysX, ParticleUniverse, etc.; all of them have been properly integrated to obtain peculiar physical and environmental effects. The stand-alone version of the application is fully compatible with Direct3D and OpenGL APIs and adopts OpenAL APIs to manage audio cards. Concluding, we devised a showcase demo which reproduces a dynamic 3D environment, including some particular effects: the alternation of day and night influencing the lighting of the scene, the rendering of terrain, water and vegetation, the reproduction of sounds and atmospheric agents.
In the area of environmental effects simulation, several methods for reproducing weather phenomena, like particle-based rain techniques, are presented in @cite_2 . The authors describe in details those methods to render, in a real-time system, very complex atmospheric physical phenomena such as strong rainfall, falling raindrops dripping off objects' surfaces, various reflections in surface materials and puddles, water ripples and puddles on the streets and so on.
@cite_2
2013275563
In this chapter we will cover approaches for creating visually complex, rich interactive environments as a case study of developing the world of ATI "ToyShop" demo. We will discuss the constraints for developing large immersive worlds in real-time, and go over the considerations for developing lighting environments for such scene rendering. Rain-specific effects in city environments will be presented. We will overview the lightning system used to create illumination from the lightning flashes, the high dynamic range rendering techniques used, various approaches for rendering rain effects and dynamic water simulation on the GPU. Methods for rendering reflections in real-time will be illustrated. Additionally, a number of specific material shaders for enhancing the feel of the rainy urban environment will be examined. This paper reports on a fast multiresolution scan matcher for vehicle localization in urban environments for self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a three-dimensional (3D) light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g., puddles and snowdrifts) or poor road surface texture. We propose a new scan matching algorithm that leverages Gaussian mixture maps to exploit the structure in the environment; these maps are a collection of Gaussian mixtures over the z-height distribution. We achieve real-time performance by developing a novel branch-and-bound, multiresolution approach that makes use of rasterized lookup tables of these Gaussian mixtures. Results are shown on two datasets that are 3.0 km: a standard trajectory and another under adverse weather conditions. The visual effects of rain are complex. Rain produces sharp intensity changes in images and videos that can severely impair the performance of outdoor vision systems. In this paper, we provide a comprehensive analysis of the visual effects of rain and the various factors that affect it. Based on this analysis, we develop efficient algorithms for handling rain in computer vision as well as for photorealistic rendering of rain in computer graphics. We first develop a photometric model that describes the intensities produced by individual rain streaks and a dynamic model that captures the spatio-temporal properties of rain. Together, these models describe the complete visual appearance of rain. Using these models, we develop a simple and effective post-processing algorithm for detection and removal of rain from videos. We show that our algorithm can distinguish rain from complex motion of scene objects and other time-varying textures. We then extend our analysis by studying how various factors such as camera parameters, rain properties and scene brightness affect the appearance of rain. We show that the unique physical properties of rain--its small size, high velocity and spatial distribution--makes its visibility depend strongly on camera parameters. This dependence is used to reduce the visibility of rain during image acquisition by judiciously selecting camera parameters. Conversely, camera parameters can also be chosen to enhance the visibility of rain. This ability can be used to develop an inexpensive and portable camera-based rain gauge that provides instantaneous rain-rate measurements. Finally, we develop a rain streak appearance model that accounts for the rapid shape distortions (i.e. oscillations) that a raindrop undergoes as it falls. We show that modeling these distortions allows us to faithfully render the complex intensity patterns that are visible in the case of raindrops that are close to the camera. This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging LIDAR scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint e.g. puddles and snowdrifts, poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional 3D LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the z -height and reflectivity distribution of the environment-which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.