Virtual reality is a new technology that has been developing a lot during the last decade. With autonomous head-mounted displays appearing on the market, new uses and needs have been created. The 3D content displayed by those devices can now be stored on distant servers rather than directly in the device's memory. In such networked immersive experiences, the 3D environment has to be streamed in real-time to the headset. In that context, several recent papers proposed utility metrics and selection strategies to schedule the streaming of the different objects composing the 3D environment, in order to minimize the latency and to optimize the quality of what is being visualized by the user at each moment. However, these proposed frameworks are hardly comparable since they operate on different systems and data. Therefore, we hereby propose an open-source DASH-based web framework for adaptive streaming of 3D content in a 6 Degrees of Freedom (DoFs) scenario. Our framework integrates several strategies and utility metrics from the state of the art, as well as several relevant features: 3D graphics compression, levels of details and the use of a visual quality index. We used our software to demonstrate the relevance of those tools and provide useful hints for the community for the further improvements of 3D streaming systems.

Over the past decade, three-dimensional (3D) graphics have become highly detailed to mimic the real world, exploding their size and complexity. Certain applications and device constraints necessitate their simplification and/or lossy compression, which can degrade their visual quality. Thus, to ensure the best Quality of Experience, it is important to evaluate the visual quality to accurately drive the compression and find the right compromise between visual quality and data size. In this work, we focus on subjective and objective quality assessment of textured 3D meshes. We first establish a large-scale dataset, which includes 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity, and corrupted by combinations of five types of compression-based distortions applied on the geometry, texture mapping, and texture image of the meshes. This dataset contains over 343k distorted stimuli. We propose an approach to select a challenging subset of 3,000 stimuli for which we collected 148,929 quality judgments from over 4,500 participants in a large-scale crowdsourced subjective experiment. Leveraging our subject-rated dataset, a learning-based quality metric for 3D graphics was proposed. Our metric demonstrates state-of-the-art results on our dataset of textured meshes and on a dataset of distorted meshes with vertex colors. Finally, we present an application of our metric and dataset to explore the influence of distortion interactions and content characteristics on the perceived quality of compressed textured meshes.

Multimedia subjective quality assessment experiments are the most prominent and reliable way to evaluate the visual quality as perceived by human observers. Along with laboratory (lab) subjective experiments, crowdsourcing (CS) experiments have become very popular in recent years, e.g., during the COVID-19 pandemic these experiments provide an alternative to lab tests. However, conducting subjective quality assessment tests in CS raises many challenges: internet connection quality, lack of control on participants’ environment, participants’ consistency and reliability, etc. In this work, we evaluate the performance of CS studies for 3D graphics quality assessment To this end, we conducted a CS experiment based on the double stimulus impairment scale method and using a dataset of 80 meshes with diffuse color information corrupted by various distortions. We compared its results with those previously obtained in a lab study conducted on the same dataset and in a virtual reality environment. Results show that under controlled conditions and with appropriate participant screening strategies, a CS experiment can be as accurate as a lab experiment.

Abstract Bidirectional Reflectance Distribution Functions (BRDFs) are pivotal to the perceived realism in image synthesis. While measured BRDF datasets are available, reflectance functions are most of the time approximated by analytical formulas for storage efficiency reasons. These approximations are often obtained by minimizing metrics such as L2—or weighted quadratic—distances, but these metrics do not usually correlate well with perceptual quality when the BRDF is used in a rendering context, which motivates a perceptual study. The contributions of this paper are threefold. First, we perform a large‐scale user study to assess the perceptual quality of 2026 BRDF approximations, resulting in 84138 judgments across 1005 unique participants. We explore this dataset and analyze perceptual scores based on material type and illumination. Second, we assess nine analytical BRDF models in their ability to approximate tabulated BRDFs. Third, we assess several image‐based and BRDF‐based (Lp, optimal transport and kernel distance) metrics in their ability to approximate perceptual similarity judgments.

Numerous methodologies for subjective quality assessment exist in the field of image processing. In particular, the Absolute Category Rating with Hidden Reference (ACR-HR), the Double Stimulus Impairment Scale (DSIS), and the Subjective Assessment Methodology for Video Quality (SAMVIQ) are considered three of the most prominent methods for assessing the visual quality of 2D images and videos. Are these methods valid/accurate to evaluate the perceived quality of 3D graphics data? Is the presence of an explicit reference necessary, due to the lack of human prior knowledge on 3D graphics data compared to natural images/videos? To answer these questions, we compare these three subjective methods (ACR-HR, DSIS, and SAMVIQ) on a dataset of high-quality colored 3D models, impaired with various distortions. These subjective experiments were conducted in a virtual reality environment. Our results show differences in the performance of the methods depending on the 3D contents and the types of distortions. We show that DSIS and SAMVIQ outperform ACR-HR in terms of accuracy and point out a stable performance. In regard to the time-effort, DSIS achieves the highest accuracy in the shortest assessment time. Results also yield interesting conclusions on the importance of a reference for judging the quality of 3D graphics. We finally provide recommendations regarding the influence of the number of observers on the accuracy.

Surface meshes associated with diffuse texture or color attributes are becoming popular multimedia contents. They provide a high degree of realism and allow six degrees of freedom (6DoF) interactions in immersive virtual reality environments. Just like other types of multimedia, 3D meshes are subject to a wide range of processing, e.g., simplification and compression, which result in a loss of quality of the final rendered scene. Thus, both subjective studies and objective metrics are needed to understand and predict this visual loss. In this work, we introduce a large dataset of 480 animated meshes with diffuse color information, and associated with perceived quality judgments. The stimuli were generated from 5 source models subjected to geometry and color distortions. Each stimulus was associated with 6 hypothetical rendering trajectories (HRTs): combinations of 3 viewpoints and 2 animations. A total of 11520 quality judgments (24 per stimulus) were acquired in a subjective experiment conducted in virtual reality. The results allowed us to explore the influence of source models, animations and viewpoints on both the quality scores and their confidence intervals. Based on these findings, we propose the first metric for quality assessment of 3D meshes with diffuse colors, which works entirely on the mesh domain. This metric incorporates perceptually-relevant curvature-based and color-based features. We evaluate its performance, as well as a number of Image Quality Metrics (IQMs), on two datasets: ours and a dataset of distorted textured meshes. Our metric demonstrates good results and a better stability than IQMs. Finally, we investigated how the knowledge of the viewpoint (i.e., the visible parts of the 3D model) may improve the results of objective metrics.

Numerous methodologies for subjective quality assessment exist in the field of image processing. In particular, the Absolute Category Rating with Hidden Reference (ACR-HR) and the Double Stimulus Impairment Scale (DSIS) are considered two of the most prominent methods for assessing the visual quality of 2D images and videos. Are these methods valid/accurate to evaluate the perceived quality of 3D graphics data? Is the presence of an explicit reference necessary, due to the lack of human prior knowledge on 3D graphics data compared to natural images/videos? To answer these questions, we compare these two subjective methods (ACR-HR and DSIS) on a dataset of high-quality colored 3D models, impaired with various distortions. These subjective experiments were conducted in a virtual reality (VR) environment. Our results show differences in the performance of the methods depending on the 3D contents and the types of distortions. We show that DSIS outperforms ACR-HR in term of accuracy and points out a stable performance. Results also yield interesting conclusions on the importance of a reference for judging the quality of 3D graphics. We finally provide recommendations regarding the influence of the number of observers on the accuracy.

We present VirtualHaus, a collaborative mixed reality application allowing two participants to recreate Mozart's apartment as it used to be by interactively placing furniture. Each participant has a different role and therefore uses a different application: the visitor uses an immersive virtual reality application, while the supervisor uses an augmented reality application. The two applications are wirelessly synchronised and display the same information with distinct viewpoints and tools.

Augmented reality aims to insert virtual objects in real scenes. In order to obtain a coherent and realistic integration, these objects have to be relighted according to their positions and real light conditions. They also have to deal with occlusion by nearest parts of the real scene. To achieve this, we have to extract photometry and geometry from the real scene. In this paper, we adapt high dynamic range reconstruction and depth estimation methods to deal with real-time constraint and consumer devices. We present their limitations along with significant parameters influencing computing time and image quality. We tune these parameters to accelerate computation and evaluate their impact on the resulting quality. To fit with the augmented reality context, we propose a real-time extraction of these information from video streams, in a single pass.

This paper presents a state of the art report on using graphics hardware for image processing and computer vision. Then we describe GPUCV, an open library for easily developing GPU accelerated image processing and analysis operators and applications

Abstract In this paper, we propose to solve the global illumination problem through a progressive rendering method relying on an adaptive sampling of the image space. The refinement of this sample scheme is driven by an image metric based on a powerful vision model. A Delaunay triangulation of the sampled points is followed by a classification of these triangles into three classes. By interpolating each triangle according to the class it belongs to, we can obtain a high quality image by computing only a fraction of all the pixels and thus saving computation time. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Perceptual Rendering, Global illumination