Scientific visualizations using physically-based lighting models play a crucial role in enhancing both image quality and realism. In the domain of medical visualization, this trend has gained significant traction under the term cinematic rendering (CR). It enables the creation of 3D photorealistic reconstructions from medical data, offering great potential for aiding healthcare professionals in the analysis and study of volumetric datasets. However, the adoption of such advanced rendering for immersive virtual reality (VR) faces two main limitations related to their high computational demands. First, these techniques are frequently used to produce pre-recorded videos and offline content, thereby restricting interactivity to predefined volume appearance and lighting settings. Second, when deployed in head-tracked VR environments they can induce cyber-sickness symptoms due to the disturbing flicker caused by noisy Monte Carlo renderings. Consequently, the scope for meaningful interactive operations is constrained in this modality, in contrast with the versatile capabilities of classical direct volume rendering (DVR). In this work, we introduce an immersive 3D medical visualization system capable of producing photorealistic and fully interactive stereoscopic visualizations on head-mounted display (HMD) devices. Our approach extends previous linear regression denoising to enable real-time stereoscopic cinematic rendering within AR/VR settings. We demonstrate the capabilities of the resulting VR system, like its interactive rendering, appearance and transfer function editing.

Abstract Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep neural networks introduce high latency on commodity hardware, limiting their usability in low-cost real-time applications. We present PlayNet, a novel approach to real-time handball play classification. Our method is based on Kalman embeddings, a new low-dimensional representation for game states that enables efficient operation on commodity hardware and customized camera layouts. Firstly, we leverage Kalman filtering to detect and track the main agents in the playing field, allowing us to represent them in a single normalized coordinate space. Secondly, we utilize a neural network trained in nonlinear dimensionality reduction through fuzzy topological data structure analysis. As a result, PlayNet achieves real-time play classification with under 55 ms of latency on commodity hardware, making it a promising addition to automated live broadcasting and game analysis pipelines.

This paper studies the adaptation of one of the most popular motion estimation methods to a massively parallel environment. To achieve the performance benefits that this and other related methods exhibit in CPU, a proper use of the GPU branching capabilities is required. We prove this claim and the viability of the branching approach, and achieve performance benefits of over ×7 compared to other fast GPU approaches. Our implemented method works very close in encoding efficiency to the reference CPU encoder for small frame sizes and high definition sequences with high bitrates, but suffers at low bitrates in high definition sequences, a fact that is common to other fast GPU implementations, but is stronger in this case due to the nature of the EPZS method. The implemented approach achieves a motion estimation performance of 722 fps on 1080p sequences using a NVIDIA GTX 580 GPU.

[Abstract] Motion estimation is known to be one of the most expensive tasks in video coding as it is usually performed through blind search-based methods. However, in the particular case of computer-generated video, the rendering stage provides useful information to speed up the process. In this paper, we propose a fast motion estimation algorithm, designed to run completely inside the GPU, to compute the optical flow required to estimate motion vectors at the same time as the graphical rendering process by using high-level information about the objects, viewpoints and effects that define each frame. The proposed method takes advantage of GPU parallelism and avoids bottlenecks in the CPU-GPU communication as the entire rendering and encoding process is performed completely inside the GPU. Avoiding search, motion estimation has very little overhead, negligible when compared with rendering and (the rest of the) video encoding costs while maintaining reasonably good quality. Performance evaluation is done with a CUDA implementation for MPEG-2 video, though results are valid for other formats, and it has been tested as part of the rendering and encoding engine of a real-world system that provides serverside visually-rich interactive applications to lightweight clients equipped with standard MPEG video decoders.

GPUs excel in parallel computations, so they are very efficient calculating the discrete cosine transform of spatial domain images, as required for video encoding. The last steps of MPEG-2 compression, however, are inherently sequential since they require a serial processing of the resulting DCT coefficients. As that can easily become a bottleneck in GPUbased video encoders, in this paper we analyze the problem of computing the zigzag scan and Huffman encoding of a MPEG- 2 coefficient block in a GPU. We observed that simply optimizing the parallelism of the serialization and compression algorithm is not enough, and it can actually lead to worse results than a simple approach with no parallelism because of inefficient memory usage, since memory accesses can dramatically slow down the computation. This paper describes three different techniques to calculate the final bit stream for a MPEG-2 quantized coefficient matrix: a simple serial implementation, a fully parallel implementation, and a combination that beats them both when considering the cost of transferring the result to the CPU.

We describe the use of an immersive, walkable VR system called The Empty Museum that the authors have developed to allow the users to walk in virtual spaces where they can experience interactive 3D cultural contents. We briefly describe the architecture of the system, to focus on the analysis of the modes of interaction between user and content in this hybrid space where the user itself takes the role of the cursor and the physical space constitutes the interface. Finally, it shows several examples of its use on a real exhibition to display contents related to local topics, from traditions to art.

Until quite recently, virtual reality systems consisted of fixed devices which enabled the user to feel immersed in a spot of the virtual space by means of the adequate hardware. The recent emergence of wireless systems for motion capture, together with the increase in graphic power of laptops, and the generalisation of wireless networks has allowed the appearance of the first systems in which at last the user is able to walk physically within a given space framed in the real one, and containing the objects and elements of the virtual space. Some examples of this hybrid space have already been accomplished worldwide. However, beyond the technical problems with the development of these systems, we must bear in mind the types of contents to be shown, making the most of the possibilities offered by the fact that the user him/herself is the pointer in this kind of virtual reality, while the space itself is the interface. The authors have recently developed a system similar to the ones described. This is a totally immersive, walkable and wireless system called the Empty Museum. The paper outlines its enlargement with the purpose of making it simultaneously usable by several persons. Besides, an example of content is provided which has been specifically designed in order to be experienced in multi-user mode with this equipment: the Virtual Art Gallery.