In this paper we address the computational inefficiency of parameter studies for automotive refrigerant circuits, which currently rely on high-fidelity but time-consuming CAE simulations. To improve time efficiency, we propose two novel machine learning models—DeepOTransformer and DeepOLSTM—trained on control values, intermediate physical parameters, power consumption, and heat exchange data. These models predict the refrigerant circuit’s physical quantities, power usage, and heat transfer rates more rapidly than conventional physics-based simulations. Compared to a baseline model, our approach achieves higher accuracy and significantly faster computation, making it a promising method for analyzing various operating conditions in automotive refrigerant systems efficiently.

Abstract Despite the significant potential of 3D object editing to impact various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often paying limited attention to the need for fine-grained control over existing 3D objects. This paper introduces a framework that uses a pre-trained regressor to enable continuous and attribute-specific modifications of both the stylistic and geometric attributes of 3D vehicle models. Here, “fine-grained control” refers to the ability to adjust specific geometric or stylistic attributes (such as roof length or perceived luxury) in a continuous and independent manner. Our method aims to preserve the identity of vehicle 3D objects and support multi-attribute editing, allowing for extensive customization while maintaining the model’s structural integrity. The framework leverages DeepSDF to obtain latent representations suitable for continuous attribute editing. Experimental results demonstrate the effectiveness of our approach in achieving detailed, controlled edits on a variety of vehicle 3D models. The code is released at https://github.com/JiangDong-miao/Vehicle_LatentEdit .

This paper presents a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. For the classification of a histologic findings, we consider an output result of a lesion endoscopic image from a pre-learned Convolutional Neural Network (CNN) as a feature vector and construct a set of Support Vector Machines (SVMs) by learning a set of the CNN feature vectors. In the video images, each frame has appearance changes such as blur, color shift, reflection of light and so on and it affects classification results. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image. We evaluated the proposed method on a customizable embedded DSP core implemented into a FPGA based prototyping system.

This paper presents a hardware implementation of a computer-aided diagnosis system that can classify lesions in real time for endoscopic video image. Unlike still images, video images contain many color shifts, blurs, reflections, and so on, a classification method that takes these noises into account is indispensable. In this paper, in the framework of Bag-of-Feature (BoF), we use a pre-learned convolutional Neural Network (CNN) with general images as feature extraction and Support Vector Machine (SVM) as classification. A robust classification method for endoscopic video is proposed by relearning SVM with several sets of endoscopic images. In addition, since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the hardware system on a customizable DSP, which can be realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). The prototyped system can be achieved real-time processing on video frame rate (30 fps @ 200 MHz). The proposed system enables not only to classify the presence or absence of cancer in the lesion but also to identify the progress of cancer and shows the clinical doctor as an objective indicator of the lesion in real time during the endoscopy examination.

In this paper, the computer-aided diagnosis system for colorectal endoscopic images is proposed. The proposed system is consisted of Convolutional Neural Network (CNN) as the feature extraction processing and Support Vector Machine (SVM) as identification processing. The proposed system is also implemented on customizable Digital Signal Processing (DSP) core: Vision P6 DSP and is demonstrated the effectiveness of a real-time recognition system by a FPGA based prototyping system, Protium™ S1.

In this paper we propose a new method for scene representation and recognition based on the concept of Region Subspaces. Each image is pre-segmented into semantically meaningful regions and local features are extracted at different scales from each such region. The Region Subspaces are the low-dimensional linear subspaces calculated from the set of local features inside each region. We also define Grassmann Matching Kernels (GMK), which extend Grassmann Kernels to be able to match simultaneously multiple subspaces. We call the resulting method, which represents image scenes through a set of Region Subspaces and matches them across different categories through the Grassmann Matching Kernel, Subspaces-of-Regions (SoR) and illustrate it on the 15-Scene dataset.

In virtual reality (VR) applications, it is often required to display a surrounding scenery with a high dynamic range (HDR) of luminance to a binocular display device such as an Oculus Rift. A surrounding scenery of an outdoor environment usually includes both bright regions, such as a sunny place and a clear sky, and dark regions, such as a shadow area. The dynamic range of the luminances between bright and dark regions are quite large.

This paper describes a method for calibrating non-overlapping cameras in a simple way: using markers on the cameras. By adding an AR (Augmented Reality) marker to a camera, we can find the transformation between the fixed AR marker and the camera's center. With such information, relative pose of cameras can be easily found as long as the marker located on them is visible. Our method consists of the following two steps: (1) use an extra camera and a chessboard to find the transformation between the AR marker and the camera center. (2) Use the information from (1) and transformation between different markers to calibrate non-overlapping cameras. Compare to other non-overlapping calibration methods, our method can work with as less as one image and does not suffer from the degenerate cases.

In this paper we propose a new local learning based regression method which utilizes ensemble-learning as a form of regularization to reduce the variance of local estimators. This makes it possible to use local learning methods even with very high-dimensional datasets. The efficacy of the proposed method is illustrated on two publicly available high-dimensional sets in comparison with several global learning methods, and it is shown that the proposed ensemble-based local learning method significantly outperforms the global ones.

Although the design of low-level local spatiotemporal features has recently led to significant improvement of performance in many action recognition applications, much less attention has been given to the equally important problem how to organize such low-level features extracted from the videos into a higher-level representation suitable to represent and discriminate between many different action classes. In this paper we propose an alternative approach to the widely-used Bag-of-Features (BoF), where instead of histograms of visual words, action sequences are represented as sets of low-dimensional linear subspaces. This results in richer and more discriminative models. In order to be able to calculate similarities between sets of subspaces we define a novel Subspace Matching Kernel. Experimental results are shown on the widely used KTH action dataset, which demonstrate the effectiveness of the proposed framework.

BACKGROUND AND OBJECTIVE: A computer-aided system for colorectal endoscopy could provide endoscopists with important helpful diagnostic support during examinations. A straightforward means of providing an objective diagnosis in real time might be for using classifiers to identify individual parts of every endoscopic video frame, but the results could be highly unstable due to out-of-focus frames. To address this problem, we propose a defocus-aware Dirichlet particle filter (D-DPF) that combines a particle filter with a Dirichlet distribution and defocus information. METHODS: We develop a particle filter with a Dirichlet distribution that represents the state transition and likelihood of each video frame. We also incorporate additional defocus information by using isolated pixel ratios to sample from a Rayleigh distribution. RESULTS: We tested the performance of the proposed method using synthetic and real endoscopic videos with a frame-wise classifier trained on 1671 images of colorectal endoscopy. Two synthetic videos comprising 600 frames were used for comparisons with a Kalman filter and D-DPF without defocus information, and D-DPF was shown to be more robust against the instability of frame-wise classification results. Computation time was approximately 88ms/frame, which is sufficient for real-time applications. We applied our method to 33 endoscopic videos and showed that the proposed method can effectively smoothen highly unstable probability curves under actual defocus of the endoscopic videos. CONCLUSION: The proposed D-DPF is a useful tool for smoothing unstable results of frame-wise classification of endoscopic videos to support real-time diagnosis during endoscopic examinations.

In this study, we have proposed an improvement for feature extraction in computer-aided diagnosis (CAD) system for colorectal endoscopic images with narrow-band imaging (NBI) magnification. Dense Scale-Invariant Feature Transform (D-SIFT) is used in the feature extraction. It is necessary to consider a trade-off between the precision of the feature extraction and speedup by the FPGA implementation for processing of real time full high definition image. In this paper, we reduced the number of dimensions for feature representation in hardware implementation purpose.

This paper proposes a method for smoothing the posterior probabilities obtained from classification results of time series input. We deal with this problem as a filtering problem with Dirichlet distribution and develop a particle filtering for this task. As a practical example of smoothing, we apply the proposed method to stabilizing NBI endoscopy recognition results over time. Experimental results demonstrate that our approach can effectively smooth highly unstable probability curves.

In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained by 3D reconstruction techniques usually differ in scale. To match scales, we estimate the ratio of scales of two given 3D point clouds. By performing PCA of spin images over different scales of two point clouds, two sets of cumulative contribution rate curves are generated. Such sets of curves can be considered to characterize the scale of the given 3D point clouds. To find the scale ratio of two point clouds, we register the two sets of curves by using a variant of ICP that estimates the ratio of scales. Simulations with the Stanford bunny and experimental results with 3D reconstructions of artificial and real scenes demonstrate that the ratio of any 3D point clouds can be effectively used for scale matching.

In this paper we propose an alternative approach to the widely-used Bag-of-Features (BoF) for representing and automatically recognizing behaviors or actions in video sequences from sets of local spatio-temporal features extracted from the videos. Instead of histograms of visual words, in the proposed framework the sets of local spatio-temporal features extracted from each video are represented as low-dimensional linear subspaces, which are further othogonalized across classes to enhance their discriminability. Similarity between videos is represented in terms of Grassmann kernels defined on the subspaces of spatio-temporal features. Experimental results on a publicly available video dataset related to classifying rodent behavior demonstrate the effectiveness of the proposed framework.

In this paper we consider the case when huge datasets need to be labeled efficiently for learning. It is assumed that the data can be naturally organized into many small groups, called chunklets, each one of which contains data from the same class, and many chunklets are available from each class. Each chunklet exhibits some of the typical variation representative for the class. We investigate how active learning methods based on uncertainty sampling perform in this setting, and whether any gains can be expected in comparison with random sampling. We also propose a novel strategy for selecting which chunklets to be selected for labeling. Experiments with 7containing variation in pose, expression and illumination conditions illustrate the proposed method.

We propose an alternative approach to the widely-used Bag-of-Features (BoF) for representing objects in terms of a collection of local features extracted from their images. In this new framework, called Subspaces-of-Features (SoF), first the sets of local features extracted from the images of the objects are represented as low-dimensional linear subspaces. Then the subspaces corresponding to different categories are orthogonalized, and the similarity between subspaces corresponding to different categories is calculated using the Grassmannian distances defined through the principal angles between the subspaces. The performance of SoF is illustrated on a standard generic object recognition benchmark.

In this paper, we propose a method to select a discriminative set of image processing operations for Linear Discriminant Analysis (LDA) as an application of the use of generating matrices representing image processing operators acting on images. First we show that generating matrices can be used for formulating LDA with increasing training samples, then analyze them as image processing operators acting on 2D continuous functions for compressing many large generating matrices by using PCA and Hermite decomposition. Then we propose Linear Discriminative Image Processing Operator Analysis, an iterative method for estimating LDA feature space along with a discriminative set of generating matrices. In experiments, we demonstrate that discriminative generating matrices outperform a non-discriminative set on the ORL and FERET datasets.

In this paper, we propose a method for estimating the rotational velocity of a table tennis ball with Inverse Compositional Image Alignment, ICIA. Assuming orthogonal projection of the camera, we derive an update rule for the motion parameters. Because of the precomputation of the Hessian matrix in ICIA and the simplifying assumptions, the motion parameters estimation at each frame is very fast. We evaluate results for measuring the spins in a synthesized sequence, as well as in real image sequences of table tennis rallies.

In this paper we show how Grassmann distances and Grassmann kernels can be efficiently used to learn and classify face sequence videos. We propose two new methods, the Grassmann Distance Mutual Subspace Method (GD-MSM) which uses Grassmann distances to define the similarity between subspaces of images, and the Grassmann Kernel Support Vector Machine (GK-SVM), which applies two Grassmann kernels - the projection kernel and the Binet-Cauchy kernel - in a convex optimization scheme, using the Support Vector Machine (SVM) framework. GD-MSM and GK-SVM are compared in a face recognition task with several related methods using a large database of face image sequences from 100 subjects, containing expression changes related to a natural conversation setting. Additionally, we study the effect of combining all available training image sequences into a single subspace per category, in comparison with using multiple smaller subspaces, i.e. representing each category by several different subspaces, where each subspace is formed from image sequences taken under different conditions.

In this paper we propose a new method for appearance-based pose estimation, called Local Procrustes Regression (LPR). In LPR, rather than learning a map between all available training samples and pose space, as is common for appearance-based pose estimation algorithms, the pose of an unknown sample is recovered locally from a small subset of the training samples, by utilizing their inter-point distances. This is accomplished by using Procrustes analysis to align the low-dimensional image subspace generated for the neighborhood of the test sample to its corresponding part of pose space. Experimental results obtained on the Object Pose Estimation Database (OPED) indicate that the proposed method performs on par with state-of-the-art methods like support vector regression.

ABSTRACT Synthesizing rainy images is a common challenge found in film, game engines, driving simulators, and architectural design. Simulating light transport through a raindrop's optical properties is a view‐dependent problem, and large quantities of raindrops are required to produce a plausible rainy scene. Accurate methods for rendering raindrops exist but are often off‐line techniques that are cost prohibitive for real‐time applications. Most real‐time solutions use textures to approximate the appearance of moving raindrops as streaks. These approaches produce plausible results but do not address the problem of temporal effects such as slow‐motion or paused simulations. In such conditions, streak‐based approximations are not suitable, and proper raindrop geometry should be considered. This paper describes a straight‐forward approach for rendering raindrops in such temporal conditions. The proposed technique consists of a preprocessing stage that generates a raindrop mask and a run‐time stage that renders raindrops as screen‐aligned billboards. The mask's contents are adjusted on the basis of the viewpoint, viewing direction, and raindrop position. The proposed method renders millions of raindrops at real‐time rates in current graphics hardware, making it suitable for applications that require high‐visual quality without compromising performance. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper we propose a new framework for view-invariant 3D object recognition, based on what we call Visibility Maps. A Visibility Map (VM) encodes a compact model of an arbitrary 3D object for which a set of images taken from different views is available. Representative local invariant features extracted from each image are selectively combined to form a visibility basis, in terms of which an arbitrary view of the modeled object can be represented. A metric which incorporates geometric information is also provided for comparing test images to the model, and can be used for recognition.

In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained by 3D reconstruction techniques usually differ in scales. To match scales, we propose a keyscale that characterizes the scale of a given 3D point cloud. By performing PCA of spin images over different scales, a keyscale is defined as the scale that gives the minimum of cumulative contribution rate of PCA at a specific dimension of eigenspace. Simulations with the Stanford bunny and experimental results with 3D reconstructions of a real scene demonstrate that keyscales of any 3D point clouds can be uniquely found and effectively used for scale matching.

No abstract available.

In this paper, we propose a volume rendering method using grid computing for large-scale volume data. Grid computing is attractive because medical institutions and research facilities often have a large number of idle computers. A large-scale volume data is divided into sub-volumes and the sub-volumes are rendered using grid computing. When using grid computing, different computers rarely have the same processor speeds. Thus the return order of results rarely matches the sending order. However order is vital when combining results to create a final image. Job- Scheduling is important in grid computing for volume rendering, so we use an obstacle-flag which changes priorities dynamically to manage sub-volume results. Obstacle-Flags manage visibility of each sub-volume when line of sight from the view point is obscured by other sub-volumes. The proposed dynamic job-scheduling based on visibility substantially increases efficiency. Our dynamic job-scheduling method was implemented on our university's campus grid and we conducted comparative experiments, which showed that the proposed method provides significant improvements in efficiency for large-scale volume rendering.

The paper proposes a method for generating a sequence of images with smooth transition of luminance distributions from two input images with different lighting conditions. The idea of the proposed method is based on image morphing. A conventional image morphing changes object shapes between two input images. We focus on changing the illumination between two images. The proposed method uses isoluminance curves as a feature primitive. Isoluminance curves acquired from images are warped based on the correspondence of the curves between two images, and transformed luminance distributions are generated from the warped isoluminance curves. The proposed method called "illumination morphing" is able to generate smooth transition of luminance between two images, and is a promising technique for many applications requiring a scene with variety of lighting effects, such as movies, TV games, and so on.

Iridescent colors appearing on natural objects, such as the feathers of a hummingbird, surfaces inside seashells, and shells of beetles, are beautiful and impressive. The cause of such iridescent colors is interference of light inside multilayer film structures covering the natural objects. The paper proposes a method for realistically rendering iridescent colors appearing on natural surfaces, taking into account interference of light inside multilayer structures. To render the iridescent colors, we have developed an illumination model for multilayer structures fluctuated through the use of a hypertexture. We also have improved the hypertexture to be able to represent fluctuated parametric curved surfaces. Several examples of iridescent colors on the surfaces of a seashell and a beetle demonstrate the usefulness of the proposed method.

This paper proposes a simple and computationally inexpensive method for animation of clouds. The cloud evolution is simulated using cellular automaton that simplifies the dynamics of cloud formation. The dynamics are expressed by several simple transition rules and their complex motion can be simulated with a small amount of computation. Realistic images are then created using one of the standard graphics APIs, OpenGL. This makes it possible to utilize graphics hardware, resulting in fast image generation. The proposed method can realize the realistic motion of clouds, shadows cast on the ground, and shafts of light through clouds.

This paper proposes a method for rendering objects coated with multilayer thin films. Multilayer thin films are widely utilized in industry for example, optical lenses, optical filters, and windowpanes, among others. Demand for visualization of the optical effects of multilayer films has therefore become great. To visualize optical properties of such films, we have developed a method for calculating composite reflectance and transmittance of the system of multilayer thin films, taking into consideration such factors as multiple reflection, interference and absorption of light inside the films. The proposed method is based on wave optics, and is able to accurately visualize the optical effects of multilayer films consisting of not only dielectric materials, but also metallic and semiconductive materials. Metallic and semiconductive materials are able to absorb light, and their refractive indices are expressed as complex values, Dielectric materials, on the other hand, do not absorb light, and the refractive indices are expressed as real values. Taking into consideration complex refractive indices, the proposed method can visualize optical phenomena caused by various kinds of multilayer thin films. The proposed method is implemented using a raytracer and optical properties of a SiO/sub 2/ film coating on a silicon base, and several kinds of multilayer films coating windowpanes or teapot are visualized to demonstrate the usefulness of the proposed method.

Rendering a scene containing water is among the most challenging aspects in the field of computer graphics. Researchers have developed a wide variety of algorithms for rendering water, but there still remain unaddressed problems due to water's multiformity and complex patterns of motion. This paper proposes a method for generating realistic animation of water droplets on transparent surfaces such as the windshield of a vehicle. One of the method's applications can be considered the animation of water droplets on a windshield, a process vital for drive simulators. The proposed method employs a particle system in a discrete environment to calculate the movement of water droplets on a surface, and environment mapping for indicating the property of transparency to quickly render scenes through transparent objects. Using the method, windshield wipers moving on a windshield and other obstacles moving against water droplets can also be taken into account. Fully computer-generated animations and video compositions of water droplets on a windshield demonstrate the usefulness of the proposed method. Copyright © 1999 John Wiley & Sons, Ltd.

In contrast with the growth of plants and trees, human organs can undergo significant changes in shape through a variety of global transformations during the growth period, such as bending or twisting. In our approach, the topology of a human organ is represented by a skeleton in the form of a tree or cycled graph. The length of skeleton growth can be simulated by an algebraic L‐system that also produces discrete events. The paper shows how to include global transformations into the formalism of L‐systems to obtain a continuous process. The shape of the organ is approximated by a number of ellipsoidal clusters centred at points on the skeleton. The proposed growth model of the organ continually responds to the positional changes of surrounding organs, thereby changing the organ shape locally. In our study, the stomach of a human embryo is used for the demonstration of organ development, and the methodology employed is also applicable to the animation of animal organs and their development.

The paper reports a solution to the problem of image interpolation for generating, with high precision, an arbitrary cross-section from a series of slices. Shape-based interpolation as proposed allows the interpolation process to be influenced by the shape of the object. It is accomplished first by creating reconstructed shapes of the objects from a series of slices. Correspondence vectors distributed along the object surface are then generated from the reconstructed shapes. These vectors ensure the correct image interpolation on the boundaries of objects. Next, the correspondence vectors are interpolated to every image point within the new cross-section in process, and the image points at the start and end of a vector are linearly interpolated. Places where the correspondence vectors cannot be (branching or holes in the object shape) are treated separately. Results are presented from biological data obtained using an optical microscope. The data indicate that the proposed method produces more accurate results than commonly used interpolation methods, although at the cost of increased computation.

A parametric L-system (Lindenmayer system) is developed to model the growth and structure changes of human stomach over the time starting in the third week and going on into week four of human embryo development. The model takes into account what is known about embryo movement during growth. The framework uses the formalism of L-systems with constructs that are dependent on time and smooth growth functions. The method captures the development of human embryo stomach consistently with static observations of the real embryo. Computer animation and visual interpretation make it possible to understand the modeled processes accurately and lead to realistic exploration of the human stomach at various stages of development.

Computer graphics are being used for visual environmental assessment or architectural designs. Displaying the sky as a background is indispensable in generating photorealistic images for such applications. In this paper, we propose a fast display method of the sky colour by expressing the intensity distribution of the sky using basis functions, even if the sun position and/or the camera position are altered. In the proposed method, cosine functions are used as basis functions. The sun altitude is altered at certain intervals and the distributions of the sky colour for each sun altitude are precalculated and stored efficiently using basis functions. The colour of the sky in the view direction of an arbitrary sun position can be obtained from the stored distributions and displayed quickly. © 1997 by John Wiley & Sons, Ltd.

Abstract Recently, computer graphics are frequently used for both architectural design and visual environmental assessment. Using computer graphics, designers can easily compare the effect of the natural light on their architectural designs under various conditions, such as different times of day, seasons, atmospheric conditions (clear or overcast sky) or building wall materials. In traditional methods of calculating the luminance due to sky light, however, all calculation must be performed from scratch if such conditions undergo change. Therefore, to compare the architectural designs under different conditions, a great deal of time has to be spent on generating the images. This paper proposes a new method of quickly generating images of an outdoor scene, taking into account glossy specular reflection, even if such conditions change. In this method, luminance due to sky light is expressed by a series of basis functions, and basis luminances corresponding to each basis function are precalculated and stored in a compressed form in the preprocess. Once the basis luminances are calculated, the luminance due to sky light can be quickly calculated by the weighted sum of the basis luminances. Several examples of an architectural design demonstrate the usefulness of the proposed method.

Abstract When designing interior lighting effects, it is desirable to compare a variety of lighting designs involving different lighting devices and directions of light. It is, however, time‐consuming to generate images with many different lighting parameters, taking interreflection into account, because all luminances must be calculated and recalculated. This makes it difficult to design lighting effects interactively. To address this problem, this paper proposes a method of quickly generating images of a given scene illustrating an interreflective environment illuminated by sources with arbitrary luminous intensity distributions. In the proposed method, the luminous intensity ditribution is expressed with basis functions. The proposed method uses a series of spherical harmonic functions as basis functions, and calculates in advance each intensity on surfaces lit by the light sources whose luminous intensity distribution are the same as the spherical harmonic functions. The proposed method makes it possible to generate images so quickly that we can change the luminous intensity distribution interactively. Combining the proposed method with an interactive walk‐through that employs intensity mapping, an interactive system for lighting design is implemented. The usefulness of the proposed method is demonstrated by its application to interactive lighting design, where many images are generated by altering lighting devices and/or direction of light.

Abstract It is inevitable for indoor lighting design to render a room lit by natural light, especially for an atelier or an indoor pool where there are many windows. This paper proposes a method for calculating the illuminance due to natural light, i.e. direct sunlight and skylight, passing through transparent planes such as window glass. The proposed method makes it possible to efficiently calculate such illuminance accurately, because it takes into account both non‐uniform luminous intensity distribution of skylight and the distribution of transparency of glass according to incident angles of light. Several examples including the lighting design in an indoor pool, are shown to demonstrate the usefulness of the proposed method.

Abstract Photorealistic animated images are extremely effective for pre‐evaluating visual impact of city renewal and construction of tall buildings. In order to generate a photorealistic image not only the direct sunlight but also skylight must be considered. This paper proposes a method of high‐fidelity image generation for photorealistic outdoor scenes based on the following ideas: The intensity distribution of skylight taking account of scattering and absorption due to particles in the atmosphere which coincides with CIE standard skylight luminance functions is sought, and realistic images considering about spectrum distribution of skylight for any altitude of the sun can be easily and accurately displayed. A rectangular parallelepiped with a specialized distribution of intensity simulating the skylight is introduced for efficient calculation of illumination due to skylight, and by employing a graphics hardware calculation of the skylight illuminance taking into account shadow effects is obtained with high efficiency; these techniques can be used to generate sequences of images, making animations possible at far lower calculation cost than previous methods.

Many techniques for rendering natural objects such as the sea, terrains, and trees have been developed; they are indispensable for flight simulators. In this paper, techniques for rendering road surfaces under various conditions are discussed. Rendering road surfaces is quite useful for the evaluation of driving safety, and it will play an important part in the development of drive simulators. Light sources with high intensity often disturb drivers especially under wet road surface conditions.This paper proposes two models, a reflection model for road surfaces taking into account weather conditions, and a model on streaks of light taking into account both refraction and diffraction of light. Some examples demonstrate the possibility of applications for drive simulators in the future.

A technique for compositing computer generated images of buildings and background images created from aerial photographs is described. The aerial photo data are mapped onto a terrain model based on cartographic data. The technique can be used to pre-evaluate visual impact of large scale construction by means of not only still images but also animations.The technique can be briefly described as follows: 1) an accurate, three-dimensional terrain model is created based on cartographic data. 2) a hierarchy of aerial photo data at various resolutions is mapped onto the terrain model as a texture. 3) the height of trees is taken into account in regions near the viewpoint. 4) allowing intersections of terrain models with constructions facilitates geometric adjustments and shadowing.