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.

With the ever-growing use of extended reality (XR) technologies, researchers seek to understand the factors leading to a higher quality of experience. Measurements of the quality of experience (e.g., presence, immersion, flow) are usually assessed through questionnaires completed after the experience. To cope with shortcomings and limitations of this kind of assessment, some recent studies tend to use physiological measures and interaction traces generated in the virtual world in replacement or in addition to questionnaires. Those physiological and behavioral measurements are still complex to implement, and existing studies difficult to replicate, because of a lack of easy and efficient tools to collect and visualize such data produced during XR experiments. In this paper, we present XRE-cho, a Unity package that allows for the recording, replaying and visualization of user behavior and interactions during XR sessions; recorded data allows to replay the whole experience, as it includes movements of the XR device, controllers and interacted objects, as well as eye tracking data (e.g., gaze position, pupils diameter). The capabilities of this tool are illustrated in a user study, where 12 participants' data have been collected and visualized with XREcho. Source code for XREcho is publicly available on GitHub.1

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.

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.

The introduction of the WebGL API for rendering 3D graphics within the browser has boosted the development of 3D Web applications. However, even with the increase of available network bandwidth, delivering 3D content on the Web still suffers from latency. An efficient way to remove the latency is to compress the 3D content in a way that allows streaming and progressive decoding i.e. by generating levels of detail (LoDs). Several solutions have been proposed for such progressive delivery of 3D web content [Ponchio and Dellepiane 2015], however they are mostly suitable for geometric information only, while most of 3D assets also own textures. The multiresolution representation and compression of textured meshes are actually difficult problems, which involve too main issues: (1) how to deal with texture seams during the LoD generation and (2) how to optimally multiplex mesh and texture LoDs in order to optimize the visual fidelity of the multi-resolution representation. In this context, we introduce a progressive compression algorithm for progressive visualization of textured 3D models on the Web.

Abstract In this paper, we present a progressive compression algorithm for textured surface meshes, which is able to handle polygonal non‐manifold meshes as well as discontinuities in the texture mapping. Our method applies iterative batched simplifications, which create high quality levels of detail by preserving both the geometry and the texture mapping. The main features of our algorithm are (1) generic edge collapse and vertex split operators suited for polygonal non‐manifold meshes with arbitrary texture seam configurations, and (2) novel geometry‐driven prediction schemes and entropy reduction techniques for efficient encoding of connectivity and texture mapping. To our knowledge, our method is the first progressive algorithm to handle polygonal non‐manifold models. For geometry and connectivity encoding of triangular manifolds and non‐manifolds, our method is competitive with state‐of‐the‐art and even better at low/medium bitrates. Moreover, our method allows progressive encoding of texture coordinates with texture seams; it outperforms state‐of‐the‐art approaches for texture coordinate encoding. We also present a bit‐allocation framework which multiplexes mesh and texture refinement data using a perceptually‐based image metric, in order to optimize the quality of levels of detail.

Abstract In this paper, we first introduce an algorithm for estimating the visual contrast on a 3D mesh. We then perform a series of psychophysical experiments to study the effects of contrast sensitivity and contrast discrimination of the human visual system for the task of differentiating between two contrasts on a 3D mesh. The results of these experiments allow us to propose a perceptual model that is able to predict whether a change in local contrast on 3D mesh, induced by a local geometric distortion, is visible or not. Finally, we illustrate the utility of the proposed perceptual model in a number of applications: we compute the Just Noticeable Distortion (JND) profile for smooth‐shaded 3D meshes and use the model to guide mesh processing algorithms.

It is common that a 3D mesh undergoes some lossy operations (e.g., compression, watermarking and transmission through noisy channels), which can introduce geometric distortions as a change in vertex position. In most cases the end users of 3D meshes are human beings; therefore, it is important to evaluate the visibility of introduced vertex displacement. In this paper we present a model for computing a Just Noticeable Distortion (JND) profile for flat-shaded 3D meshes. The proposed model is based on an experimental study of the properties of the human visual system while observing a flat-shaded 3D mesh surface, in particular the contrast sensitivity function and contrast masking. We first define appropriate local perceptual properties on 3D meshes. We then detail the results of a series of psychophysical experiments where we have measured the threshold needed for a human observer to detect the change in vertex position. These results allow us to compute the JND profile for flat-shaded 3D meshes. The proposed JND model has been evaluated via a subjective experiment, and applied to guide 3D mesh simplification as well as to determine the optimal vertex coordinates quantization level for a 3D model.

Passive microwave remote sensing is extensively used in polar regions to study the cryosphere. To better understand the measured signal above continental ice-covered areas, our objective is to estimate the microwave emission of bubbly-ice surfaces using a physically based multilayer electromagnetic model, i.e., the dense media radiative transfer-multilayer model (DMRT-ML). This model accounts for ice layers with variable amounts of bubbles. Each layer is fully described by its temperature, density, thickness, and air bubble radius. Simulations are performed using in situ data from two distinct sites: one in Antarctica on a coastal Blue Ice Area near the Cap Prud'Homme (CPH) station in Adelie Land and the other on the Barnes Ice Cap (BIC) located on Baffin Island in the Arctic. On this ice cap, superimposed ice with seasonal snow cover about 1 m thick was observed. In both cases, several ice parameters were measured or estimated, and the others were optimized. Results of the DMRT-ML simulations are compared with in situ surface-based radiometer (SBR) measurements at 11, 19, and 37 GHz at both horizontal and vertical polarizations. Results show that DMRT-ML is able to reproduce the microwave emission of different ice types with good accuracy when accounting for ice bubbles: final RMSE = 7.37 K and 8.42 K, for CPH and BIC, respectively, compared with RMSE ranging from 15 K to 40 K without bubbles. Comparisons between SBR measurements and satellite data for the BIC also show good agreement (RMSE = 4.1 K for 19 and 37 GHz, both polarizations).

Snow grain size is the snowpack parameter that most affects the microwave snow emission. The specific surface area (SSA) of snow is a metric that allows rapid and reproducible field measurements and that well represents the grain size. However, this metric cannot be used directly in microwave snow emission models (MSEMs). The aim of this paper is to evaluate the suitability and the adaptations required for using the SSA in two MSEMs, i.e., the Dense Media Radiative Theory-Multilayer model (DMRT-ML) and the Helsinki University of Technology model (HUT n-layer), based on in situ radiometric measurements. Measurements of the SSA, using snow reflectance in the short-wave infrared, were taken at 20 snowpits in various environments (e.g., grass, tundra, and dry fen). The results show that both models required a scaling factor for the SSA values to minimize the root-mean-square error between the measured and simulated brightness temperatures. For DMRT-ML, the need for a scaling factor is likely due to the oversimplified representation of snow as spheres of ice with a uniform radius. We hypothesize that the need for a scaling factor is related to the grain size distribution of snow and the stickiness between grains. For HUT n-layer, using the SSA underestimates the attenuation by snow, particularly for snowpacks with a significant amount of depth hoar. This paper provides a reliable description of the grain size for DMRT-ML, which is of particular interest for the assimilation of satellite passive microwave data in snow models.

Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50°N to 60 °N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gradient in fraction and stem volume. A large database (>; 2000 points with a stem volume ranging between 0 and 700 m3·ha-1) showed that brightness temperatures (Tb) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore,Tbvalues saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polarization), respectively. These relationships were used to estimate snowTbfrom the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas.

The exact Minkowski sum of polyhedra is of particular interest in many applications, ranging from image analysis and processing to computer-aided design and robotics. Its computation and implementation is a difficult and complicated task when nonconvex polyhedra are involved. We present the NCC-CVMS algorithm, an exact and efficient contributing vertices-based Minkowski sum algorithm for the computation of the Minkowski sum of a nonconvex--convex pair of polyhedra, which handles nonmanifold situations and extracts eventual polyhedral holes inside the Minkowski sum outer boundary. Our algorithm does not output boundaries that degenerate into a polyline or a single point. First, we generate a superset of the Minkowski sum facets through the use of the contributing vertices concept and by summing only the features (facets, edges, and vertices) of the input polyhedra which have coincident orientations. Secondly, we compute the 2D arrangements induced by the superset triangles intersections. Finally, we obtain the Minkowski sum through the use of two simple properties of the input polyhedra and the Minkowski sum polyhedron itself, that is, the closeness and the two-manifoldness properties. The NCC-CVMS algorithm is efficient because of the simplifications induced by the use of the contributing vertices concept, the use of 2D arrangements instead of 3D arrangements which are difficult to maintain, and the use of simple properties to recover the Minkowski sum mesh. We implemented our NCC-CVMS algorithm on the base of CGAL and used exact number types. More examples and results of the NCC-CVMS algorithm can be found at: http://liris.cnrs.fr/hichem.barki/mksum/NCC-CVMS

We present a new algorithm, based on the concept of contributing vertices, for the exact and efficient computation of the Minkowski difference of convex polyhedra. First, we extend the concept of contributing vertices for the Minkowski difference case. Then, we generate a Minkowski difference facets superset by exploiting the information provided by the computed contributing vertices. Finally, we compute the Minkowski difference polyhedron through the trimming of the generated superset. We compared our Contributing Vertices-based Minkowski Difference (CVMD) algorithm to a Nef polyhedra-based approach using Minkowski addition, complement, transposition, and union operations. The performance benchmark shows that our CVMD algorithm outperforms the indirect Nef polyhedra-based approach. All our implementations use exact number types, produce exact results, and are based on CGAL, the Computational Geometry Algorithms Library.

We present an original approach for the computation of the Minkowski sum of a non-convex polyhedron without fold and a convex polyhedron, without decomposition and union steps-that constitute the bottleneck of convex decomposition-based algorithms. A non-convex polyhedron without fold is a polyhedron whose boundary is completely recoverable from three orthographic projections defined by three orthogonal basis vectors in Ropf3. First, we generate a superset of the Minkowski sum facets using the concept of contributing vertices we accommodate for a non-convex-convex pair of polyhedra. The generated superset guarantees that its envelope is the boundary of the Minkowski sum polyhedron. Secondly, we extract the Minkowski sum facets and handle the intersections among the superset facets by using 3D envelope computation. Our approach is limited to non-convex polyhedra without fold because of the use of 3D envelope computation to recover the Minkowski sum boundary. Models with holes are not handled by our method. The implementation of our algorithm uses exact number types, produces exact results, and is based on CGAL, the Computational Geometry Algorithms Library.

During the last decades, the three-dimensional objects have begun to compete with traditional multimedia (images, sounds and videos) and have been used by more and more applications. The common model used to represent them is a surfacic mesh due to its intrinsic simplicity and efficacity. In this paper, we present a new algorithm for the segmentation of semi-regular triangle meshes, via multiresolution analysis. Our method uses several measures which reflect the roughness of the surface for all meshes resulting from the decomposition of the initial model into different fine-to-coarse multiresolution meshes. The geometric data decomposition is based on the lifting scheme. Using that formulation, we have compared various interpolant prediction operators, associated or not with an update step. For each resolution level, the resulting approximation mesh is then partitioned into classes having almost constant roughness thanks to a clustering algorithm. Resulting classes gather regions having the same visual appearance in term of roughness. The last step consists in decomposing the mesh into connex groups of triangles using region growing ang merging algorithms. These connex surface patches are of particular interest for adaptive mesh compression, visualisation, indexation or watermarking.

Abstract In this paper we present a new framework for subdivision surface approximation of three‐dimensional models represented by polygonal meshes. Our approach, particularly suited for mechanical or Computer Aided Design (CAD) parts, produces a mixed quadrangle‐triangle control mesh, optimized in terms of face and vertex numbers while remaining independent of the connectivity of the input mesh. Our algorithm begins with a decomposition of the object into surface patches. The main idea is to approximate the region boundaries first and then the interior data. Thus, for each patch, a first step approximates the boundaries with subdivision curves (associated with control polygons) and creates an initial subdivision surface by linking the boundary control points with respect to the lines of curvature of the target surface. Then, a second step optimizes the initial subdivision surface by iteratively moving control points and enriching regions according to the error distribution. The final control mesh defining the whole model is then created assembling every local subdivision control meshes. This control polyhedron is much more compact than the original mesh and visually represents the same shape after several subdivision steps, hence it is particularly suitable for compression and visualization tasks. Experiments conducted on several mechanical models have proven the coherency and the efficiency of our algorithm, compared with existing methods.

This work presents a new and efficient algorithm for the decomposition of 3D arbitrary triangle meshes into surface patches. The algorithm is based on the curvature tensor field analysis and presents two distinct complementary steps: a region based segmentation, which is an improvement of that presented by [G. Lavoue et al., (2004)] and which decomposes the object into known and near constant curvature patches, and a boundary rectification based on curvature tensor directions, which corrects boundaries by suppressing their artifacts or discontinuities. Experiments were conducted on various models including both CAD and natural objects, results are satisfactory. Resulting segmented patches, by virtue of their properties (known curvature, clean boundaries) are particularly adapted to computer graphics tasks like parametric or subdivision surface fitting in an adaptive compression objective.

3D discretized objects become more and more present in many applications for computer graphics. The need to store and transmit these data grows permanently. This paper introduces a new scheme of 3D object progressive transmission. It is based on an union of cone sections and balls selected from the medial axis and optimized for a better scalability in terms of bit rates and quality. This representation of a 3D object is a lossless approach and is very efficient for compression and progressive transmission, as shown in the results presented in this paper.