A Webware For Computer Graphics Education

Computer-assisted surveillance of complex environments is becoming more and more interesting, thanks to significant improvements in real-time signal processing. In the surveillance research field applied to public areas, crowd monitoring is very useful but presents particularly complex problems. Recognizing human bodies and tracking their movements in complex real scenes by using a sequence of images are among the most difficult tasks in computer vision. The main difficulties arise both from the high level of shape variability associated with moving human bodies, and from the complexity of environmental real scenes. A method for tracking human motion in 3D real scenes by means of Kalman filtering is presented It is based on the introduction of a suitable mathematical model for a human body moving in a 3D scene and interacting with the surrounding environment. The developed refined static and dynamic models allow the system to be accurate and robust. Due to its real-time functioning, accuracy and robustness, the method can be used in surveillance systems devoted to assure public safety.

As a frontier technology, holography has important research values in fields such as bio-micrographic imaging, light field modulation and data storage. However, the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough, which has seriously hindered the development of holography. Here, a novel holographic camera is proposed to solve the above inherent problems completely. The proposed holographic camera consists of the acquisition end and the calculation end. At the acquisition end of the holographic camera, specially configured liquid materials and liquid lens structure based on voice-coil motor-driving are used to produce the liquid camera, so that the liquid camera can quickly capture the focus stack of the real 3D scene within 15 ms. At the calculation end, a new structured focus stack network (FS-Net) is designed for hologram calculation. After training the FS-Net with the focus stack renderer and learnable Zernike phase, it enables hologram calculation within 13 ms. As the first device to achieve real-time incoherent acquisition and high-fidelity holographic reconstruction of a real 3D scene, our proposed holographic camera breaks technical bottlenecks of difficulty in acquiring the real 3D scene, low quality of the holographic reconstructed image, and incorrect defocus blur. The experimental results demonstrate the effectiveness of our holographic camera in the acquisition of focal plane information and hologram calculation of the real 3D scene. The proposed holographic camera opens up a new way for the application of holography in fields such as 3D display, light field modulation, and 3D measurement.

Three-dimensional (3D) scene models are essential for how humans and intelligent systems perceive, reason about, and interact with the world. From digital avatars and product design to indoor environments and urban landscapes, 3D data provides semantic and spatial context that 2D data alone cannot fully capture. However, acquiring detailed and realistic 3D scenes remains a major bottleneck in terms of accessibility and scalability. Conventional approaches are typically resource-expensive, labor-intensive and often not friendly for non-expert users. Powered by advances in both deep generative architectures and multimodal learning paradigms, generative models have emerged as a promising alternative. Therefore, the need for controllable, coherent, and functionally meaningful 3D scene generation from diverse input modalities defines the central problem of this dissertation: developing generative models that can synthesize realistic 3D scenes under multimodal supervision. We begin at the object level, tackling the challenging task of generating flexible and controllable 3D point clouds from a single image, especially for general and structurally complex objects such as buildings. Extending beyond individual objects, we investigate multi-object 3D scene generation with an emphasis on coherence and functional usability. Specifically, we explore how to incorporate global semantics, layout regularization, and human interaction priors into generative pipelines to ensure that the synthesized scenes are not only visually realistic but also functionally aligned with human-aware functionality.<br/><br/>

During the fieldwork of hydraulic engineering, practical engineers normally document geological information manually. Although there are some GIS-based digital tools for geology, they are not perfectly applicable to hydraulic engineering. As a result, the current work mode is ineffective, unmanageable, error-prone, and not conducive to subsequent analysis. To address this problem, we developed a digital tool which enables geological recording and quick modeling based on 3D real scenes in the field of hydropower projects. There are three modules in the surface tool: object recording, image interpretation, and field analysis. The object recording module is to mark geological points (e.g., drills and shafts), lines (e.g., faults, stratigraphic boundaries), and surfaces (e.g., slope and stocking yard) on a 3D scene and then store them in the database. The image interpretation is to interpret the 2D information in images to 3D models loaded in 3D software for further studies, such as GOCAD. The field analysis includes surface fitting, stability analysis of blocks, occurrences calculating, rock recognition, and 69/sketching. The tool is helpful for recording data, drawing geological boundaries, and building a preliminary model in the geological survey.

As an important technology of digital construction, real 3D models can improve the immersion and realism of virtual reality (VR) scenes. The large amount of data for real 3D scenes requires more effective rendering methods, but the current rendering optimization methods have some defects and cannot render real 3D scenes in virtual reality. In this study, the location of the viewing frustum is predicted by a Kalman filter, and eye-tracking equipment is used to recognize the region of interest (ROI) in the scene. Finally, the real 3D model of interest in the predicted frustum is rendered first. The experimental results show that the method of this study can predict the frustrum location approximately 200 ms in advance, the prediction accuracy is approximately 87%, the scene rendering efficiency is improved by 8.3%, and the motion sickness is reduced by approximately 54.5%. These studies help promote the use of real 3D models in virtual reality and ROI recognition methods. In future work, we will further improve the prediction accuracy of viewing frustums in virtual reality and the application of eye tracking in virtual geographic scenes.

Three-dimensional (3D) light-field display has achieved a great improvement. However, the collection of dense viewpoints in the real 3D scene is still a bottleneck. Virtual views can be generated by unsupervised networks, but the quality of different views is inconsistent because networks are separately trained on each posed view. Here, a virtual view synthesis method for the 3D light-field display based on scene tower blending is presented, which can synthesize high quality virtual views with correct occlusions by blending all tower results, and dense viewpoints on 3D light-field display can be provided with smooth motion parallax. Posed views are combinatorially input into diverse unsupervised CNNs to predict respective input-view towers, and towers of the same viewpoint are fused together. All posed-view towers are blended as a scene color tower and a scene selection tower, so that 3D scene distributions at different depth planes can be accurately estimated. Blended scene towers are soft-projected to synthesize virtual views with correct occlusions. A denoising network is used to improve the image quality of final synthetic views. Experimental results demonstrate the validity of the proposed method, which shows outstanding performances under various disparities. PSNR of the virtual views are about 30 dB and SSIM is above 0.91. We believe that our view synthesis method will be helpful for future applications of the 3D light-field display.

The appearance mode of an object, whether it appears selfluminous or reflective, depends on its luminance and its surrounding. This research aims to verify whether the appearance mode of a spherical lamp ("on" / "off") and perceived room brightness is influenced by the presentation medium: real 3D scenes (R-3D), rendered virtual 3D scenes (VR-3D) presented on a head-mounted-display (HMD) and 2D scenes presented on a regular display (D-2D). Twenty observers evaluated the lamp's appearance mode when presented in different luminance values and rated the apparent room brightness of the scene under four viewing conditions: R3D and D-2D with warm-white scene lighting, and D-2D and VR-3D with cool-white scene lighting. Threshold luminance, defined as the luminance corresponding to a 50-50 chance of perceiving a lamp as switched on, showed large observer variability, which might originate from the diversity of the observers' understanding of the lamp material and their strategy to judge the appearance mode. Respectively, threshold luminance and room brightness were significantly lower and significantly higher for the virtual reality scene than for the other conditions. However, no evidence was found that the appearance mode of a spherical lamp can relevantly predict room brightness.

In an immersive tele-presence environment a 3D remote real scene is projected from the viewpoint of the local user. This 3D world is acquired through stereo reconstruction at the remote site. In this paper we start a performance analysis of stereo algorithms with respect to the task of immersive visualization. As opposed to usual monocular image based rendering, we are also interested in the depth error in novel views because our rendering is stereoscopic. We describe an evaluation test-bed which provides a world-wide first available set of registered dense ground-truth laser data and image data from multiple views. We establish metrics for novel depth views that reflect discrepancies both in the image and in 3D-space. It is well known that stereo performance is affected by both erroneous matching as well as incorrect depth triangulation. We experimentally study the effects of occlusion and low texture on the distributions of the error metrics. Then, we algebraically predict the behavior of depth and novel projection error as a function of the camera set-up and the error in the disparity. These are first steps towards building a laboratory for psychophysical judgement of depth estimates which is the ultimate performance test of tele-presence stereo.

By moving a depth sensor around a room, we compute a 3D CAD model of the environment, capturing the room shape and contents such as chairs, desks, sofas, and tables. Rather than reconstructing geometry, we match, place, and align each object in the scene to thousands of CAD models of objects. In addition to the fully automatic system, the key technical contribution is a novel approach for aligning CAD models to 3D scans, based on deep reinforcement learning. This approach, which we call Learning-based ICP, outperforms prior ICP methods in the literature, by learning the best points to match and conditioning on object viewpoint. LICP learns to align using only synthetic data and does not require ground truth annotation of object pose or keypoint pair matching in real scene scans. While LICP is trained on synthetic data and without 3D real scene annotations, it outperforms both learned local deep feature matching and geometric based alignment methods in real scenes. The proposed method is evaluated on real scenes datasets of SceneNN and ScanNet as well as synthetic scenes of SUNCG. High quality results are demonstrated on a range of real world scenes, with robustness to clutter, viewpoint, and occlusion.

Expressing mental images visually as 3D scenes is a time-consuming challenge. Therefore, we employ natural language to facilitate the creation of virtual environments. In this paper, we present a framework, which automatically converts an arbitrary descriptive text into a representative 3D scene. Our system parses a user-written input text, extracts information using techniques from Natural Language Processing (NLP) and identifies relevant units. Based on derived object-to-object relations, our system associates every object with an appropriate 3D model and evaluates spatial dependencies of the entities. The resulting locations are combined based on adequate heuristics in order to create natural looking virtual environments. Finally, a physics engine is used to render a realistic and interactive 3D scene which enables the user to actively manipulate the stage setup.

Currently, the route planning functions in 2D/3D campus navigation systems in the market are unable to process indoor and outdoor localization information simultaneously, and the UI experiences are not optimal because they are limited by the service platforms. An ARCore-based augmented reality campus navigation system is designed in this paper in order to solve the relevant problems. Firstly, the proposed campus navigation system uses ARCore to enhance reality by presenting 3D information in real scenes. Secondly, a visual inertial ranging algorithm is proposed for real-time locating and map generating in mobile devices. Finally, rich Unity3D scripts are designed in order to enhance users’ autonomy and enjoyment during navigation experience. In this paper, indoor navigation and outdoor navigation experiments are carried out at the Lingang campus of Shanghai University of Electric Power. Compared with the AR outdoor navigation system of Gaode, the proposed AR system can achieve increased precise outdoor localization by deploying the visual inertia odometer on the mobile phone and realizes the augmented reality function of 3D information and real scene, thus enriching the user’s interactive experience. Furthermore, four groups of students have been selected for system testing and evaluation. Compared with traditional systems, such as Gaode map or Internet media, experimental results show that our system could facilitate the effectiveness and usability of learning on campus.

Abstract. 3D Real Scene is an important part of new infrastructure construction, which provides a unified spatial base for economic and social development and informatization of various departments. According to the demand of real 3D Real Scene digital construction, this paper aims to study the method of indoor and outdoor structured monomer reconstruction of city 3D Real Scene, develop digital twin platform, and strive to lead the development of 3D Real Scene. The main research contents of this paper are as follows: 1) Spatio-temporal-spectral-angular remote sensing observation system and data fusion model; 2) Rapid construction method of vector monomer 3D model of indoor and outdoor entity object; 3) 3D structural reconstruction technology of urban component level based on vector images; 4) A universal digital twins platform, LuojiaDT. The technologies we have developed have been widely used in the national 3D Real Scene construction of China, including smart city, smart transportation, cultural heritage protection, public security and police, urban underground pipe network, indoor and outdoor location service. This research will promote the continuous development of digital twins technology.

An Intro to RenderMan and Procedural Shading in Pixar's RenderMan Studio (for geeks and artists)Proceduralism is a powerful concept in computer graphics. It facilitates scenes of enormous scale, exquisite varieties of detail, and impressive efficiency. However, artists who are fluent in procedural techniques are still rare, and many studios miss out on the possibilities that this exciting field offers. This course explores how to create procedural shaders without programming, using Pixar's industry standard renderer, RenderMan, and its Autodesk Maya-based front-end, RenderMan Studio.The first section of the course is an overview of RenderMan, its history, use in the industry, important features, and how it works. Topics include the Reyes pipeline and how it has helped to create some of the most impressive visuals in computer graphics, and proceduralism: its pros and cons, both in general and as it applies to shading.The second section is a live demonstration of how to create a procedural animated shader for an orange that ages over time, from unripe to fresh to old and dusty. The demo begins with a sphere in Maya, then shows how to create all the detail using a shading network in RenderMan Studio. No textures are used. The look is created entirely with noise, splines, displacement, and more! Finally, the course presents examples of how the techniques used in shading the orange apply in industry and beyond.

Part I of this book gives an overview of principles and concepts of computer graphics on the basis of the Graphical Kernel System. Although the concepts described are closely related to GKS, most of them are general to a wide spectrum of computer graphics systems. This is true for the description of interfaces, including the interface to computer graphics users, and for the principles and goals of computer graphics standards as well as for the basic concepts of GKS. The dealing with concepts and methods has to be based on a common understanding. This requires a suitable terminology. The terminology used in this book is taken from the ISO Data Processing Vocabulary [ISO82a]. The set of terms defined in the GKS standard document [GKS82] is overlapping the ISO definitions for computer graphics to a great extent. Terms from both documents that are most important in our context are defined and explained in the following chapters. Most definitions are taken literally from the GKS document.

Pertinent Concepts in Computer Graphics: Proceedings of the Second University of Illinois Conference on Computer Graphics