Performance and quality analysis of convolution-based volume illumination

Convolution-based techniques for volume rendering are among the fastest in the on-the-fly volumetric illumination category. Such methods, however, are still considerably slower than conventional local illumination techniques. In this paper we describe how to adapt two commonly used strategies for reducing aliasing artifacts, namely pre-integration and supersampling, to such techniques. These strategies can help reduce the sampling rate of the lighting information (thus the number of convolutions), bringing considerable performance benefits. We present a comparative analysis of their effectiveness in offering performance improvements. We also analyze the (negligible) differences they introduce when comparing their output to the reference method. These strategies can be highly beneficial in setups where direct volume rendering of continuously streaming data is desired and continuous recomputation of full lighting information is too expensive, or where memory constraints make it preferable not to keep additional precomputed volumetric data in memory. In such situations these strategies make single pass, convolution-based volumetric illumination models viable for a broader range of applications, and this paper provides practical guidelines for using and tuning such strategies to specific use cases

Drivable 3D Gaussian Avatars

We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.Comment: Website: https://zielon.github.io/d3ga

HOLOGRAPHICS: Combining Holograms with Interactive Computer Graphics

Among all imaging techniques that have been invented throughout the last decades, computer graphics is one of the most successful tools today. Many areas in science, entertainment, education, and engineering would be unimaginable without the aid of 2D or 3D computer graphics. The reason for this success story might be its interactivity, which is an important property that is still not provided efficiently by competing technologies – such as holography. While optical holography and digital holography are limited to presenting a non-interactive content, electroholography or computer generated holograms (CGH) facilitate the computer-based generation and display of holograms at interactive rates [2,3,29,30]. Holographic fringes can be computed by either rendering multiple perspective images, then combining them into a stereogram [4], or simulating the optical interference and calculating the interference pattern [5]. Once computed, such a system dynamically visualizes the fringes with a holographic display. Since creating an electrohologram requires processing, transmitting, and storing a massive amount of data, today’s computer technology still sets the limits for electroholography. To overcome some of these performance issues, advanced reduction and compression methods have been developed that create truly interactive electroholograms. Unfortunately, most of these holograms are relatively small, low resolution, and cover only a small color spectrum. However, recent advances in consumer graphics hardware may reveal potential acceleration possibilities that can overcome these limitations [6]. In parallel to the development of computer graphics and despite their non-interactivity, optical and digital holography have created new fields, including interferometry, copy protection, data storage, holographic optical elements, and display holograms. Especially display holography has conquered several application domains. Museum exhibits often use optical holograms because they can present 3D objects with almost no loss in visual quality. In contrast to most stereoscopic or autostereoscopic graphics displays, holographic images can provide all depth cues—perspective, binocular disparity, motion parallax, convergence, and accommodation—and theoretically can be viewed simultaneously from an unlimited number of positions. Displaying artifacts virtually removes the need to build physical replicas of the original objects. In addition, optical holograms can be used to make engineering, medical, dental, archaeological, and other recordings—for teaching, training, experimentation and documentation. Archaeologists, for example, use optical holograms to archive and investigate ancient artifacts [7,8]. Scientists can use hologram copies to perform their research without having access to the original artifacts or settling for inaccurate replicas. Optical holograms can store a massive amount of information on a thin holographic emulsion. This technology can record and reconstruct a 3D scene with almost no loss in quality. Natural color holographic silver halide emulsion with grain sizes of 8nm is today’s state-of-the-art [14]. Today, computer graphics and raster displays offer a megapixel resolution and the interactive rendering of megabytes of data. Optical holograms, however, provide a terapixel resolution and are able to present an information content in the range of terabytes in real-time. Both are dimensions that will not be reached by computer graphics and conventional displays within the next years – even if Moore’s law proves to hold in future. Obviously, one has to make a decision between interactivity and quality when choosing a display technology for a particular application. While some applications require high visual realism and real-time presentation (that cannot be provided by computer graphics), others depend on user interaction (which is not possible with optical and digital holograms). Consequently, holography and computer graphics are being used as tools to solve individual research, engineering, and presentation problems within several domains. Up until today, however, these tools have been applied separately. The intention of the project which is summarized in this chapter is to combine both technologies to create a powerful tool for science, industry and education. This has been referred to as HoloGraphics. Several possibilities have been investigated that allow merging computer generated graphics and holograms [1]. The goal is to combine the advantages of conventional holograms (i.e. extremely high visual quality and realism, support for all depth queues and for multiple observers at no computational cost, space efficiency, etc.) with the advantages of today’s computer graphics capabilities (i.e. interactivity, real-time rendering, simulation and animation, stereoscopic and autostereoscopic presentation, etc.). The results of these investigations are presented in this chapter

Dynamic Volume Rendering of Functional Medical Data on Dissimilar Hardware Platforms

In the last 30 years, medical imaging has become one of the most used diagnostic tools in the medical profession. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) technologies have become widely adopted because of their ability to capture the human body in a non-invasive manner. A volumetric dataset is a series of orthogonal 2D slices captured at a regular interval, typically along the axis of the body from the head to the feet. Volume rendering is a computer graphics technique that allows volumetric data to be visualized and manipulated as a single 3D object. Iso-surface rendering, image splatting, shear warp, texture slicing, and raycasting are volume rendering methods, each with associated advantages and disadvantages. Raycasting is widely regarded as the highest quality renderer of these methods. Originally, CT and MRI hardware was limited to providing a single 3D scan of the human body. The technology has improved to allow a set of scans capable of capturing anatomical movements like a beating heart. The capturing of anatomical data over time is referred to as functional imaging. Functional MRI (fMRI) is used to capture changes in the human body over time. While fMRI’s can be used to capture any anatomical data over time, one of the more common uses of fMRI is to capture brain activity. The fMRI scanning process is typically broken up into a time consuming high resolution anatomical scan and a series of quick low resolution scans capturing activity. The low resolution activity data is mapped onto the high resolution anatomical data to show changes over time. Academic research has advanced volume rendering and specifically fMRI volume rendering. Unfortunately, academic research is typically a one-off solution to a singular medical case or set of data, causing any advances to be problem specific as opposed to a general capability. Additionally, academic volume renderers are often designed to work on a specific device and operating system under controlled conditions. This prevents volume rendering from being used across the ever expanding number of different computing devices, such as desktops, laptops, immersive virtual reality systems, and mobile computers like phones or tablets. This research will investigate the feasibility of creating a generic software capability to perform real-time 4D volume rendering, via raycasting, on desktop, mobile, and immersive virtual reality platforms. Implementing a GPU-based 4D volume raycasting method for mobile devices will harness the power of the increasing number of mobile computational devices being used by medical professionals. Developing support for immersive virtual reality can enhance medical professionals’ interpretation of 3D physiology with the additional depth information provided by stereoscopic 3D. The results of this research will help expand the use of 4D volume rendering beyond the traditional desktop computer in the medical field. Developing the same 4D volume rendering capabilities across dissimilar platforms has many challenges. Each platform relies on their own coding languages, libraries, and hardware support. There are tradeoffs between using languages and libraries native to each platform and using a generic cross-platform system, such as a game engine. Native libraries will generally be more efficient during application run-time, but they require different coding implementations for each platform. The decision was made to use platform native languages and libraries in this research, whenever practical, in an attempt to achieve the best possible frame rates. 4D volume raycasting provides unique challenges independent of the platform. Specifically, fMRI data loading, volume animation, and multiple volume rendering. Additionally, real-time raycasting has never been successfully performed on a mobile device. Previous research relied on less computationally expensive methods, such as orthogonal texture slicing, to achieve real-time frame rates. These challenges will be addressed as the contributions of this research. The first contribution was exploring the feasibility of generic functional data input across desktop, mobile, and immersive virtual reality. To visualize 4D fMRI data it was necessary to build in the capability to read Neuroimaging Informatics Technology Initiative (NIfTI) files. The NIfTI format was designed to overcome limitations of 3D file formats like DICOM and store functional imagery with a single high-resolution anatomical scan and a set of low-resolution anatomical scans. Allowing input of the NIfTI binary data required creating custom C++ routines, as no object oriented APIs freely available for use existed. The NIfTI input code was built using C++ and the C++ Standard Library to be both light weight and cross-platform. Multi-volume rendering is another challenge of fMRI data visualization and a contribution of this work. fMRI data is typically broken into a single high-resolution anatomical volume and a series of low-resolution volumes that capture anatomical changes. Visualizing two volumes at the same time is known as multi-volume visualization. Therefore, the ability to correctly align and scale the volumes relative to each other was necessary. It was also necessary to develop a compositing method to combine data from both volumes into a single cohesive representation. Three prototype applications were built for the different platforms to test the feasibility of 4D volume raycasting. One each for desktop, mobile, and virtual reality. Although the backend implementations were required to be different between the three platforms, the raycasting functionality and features were identical. Therefore, the same fMRI dataset resulted in the same 3D visualization independent of the platform itself. Each platform uses the same NIfTI data loader and provides support for dataset coloring and windowing (tissue density manipulation). The fMRI data can be viewed changing over time by either animation through the time steps, like a movie, or using an interface slider to “scrub” through the different time steps of the data. The prototype applications data load times and frame rates were tested to determine if they achieved the real-time interaction goal. Real-time interaction was defined by achieving 10 frames per second (fps) or better, based on the work of Miller [1]. The desktop version was evaluated on a 2013 MacBook Pro running OS X 10.12 with a 2.6 GHz Intel Core i7 processor, 16 GB of RAM, and a NVIDIA GeForce GT 750M graphics card. The immersive application was tested in the C6 CAVE™, a 96 graphics node computer cluster comprised of NVIDIA Quadro 6000 graphics cards running Red Hat Enterprise Linux. The mobile application was evaluated on a 2016 9.7” iPad Pro running iOS 9.3.4. The iPad had a 64-bit Apple A9X dual core processor with 2 GB of built in memory. Two different fMRI brain activity datasets with different voxel resolutions were used as test datasets. Datasets were tested using both the 3D structural data, the 4D functional data, and a combination of the two. Frame rates for the desktop implementation were consistently above 10 fps, indicating that real-time 4D volume raycasting is possible on desktop hardware. The mobile and virtual reality platforms were able to perform real-time 3D volume raycasting consistently. This is a marked improvement for 3D mobile volume raycasting that was previously only able to achieve under one frame per second [2]. Both VR and mobile platforms were able to raycast the 4D only data at real-time frame rates, but did not consistently meet 10 fps when rendering both the 3D structural and 4D functional data simultaneously. However, 7 frames per second was the lowest frame rate recorded, indicating that hardware advances will allow consistent real-time raycasting of 4D fMRI data in the near future