The large-scale integration of robots in agriculture offers many promises for enhancing sustainability and increasing food production. The numerous applications of agricultural robots rely on the transmission of data via mobile network, with the amount of data depending on the services offered by the robots and the level of on-board technology. Nevertheless, infrastructure required to deploy these robots, as well as the related energy and environmental consequences, appear overlooked in the digital agriculture literature. In this study, we propose a method for assessing the additional energy consumption and carbon footprint induced by a large-scale deployment of agricultural robots. Our method also estimates the share of agricultural area that can be managed by the deployed robots with respect to network infrastructure constraints. We have applied this method to metropolitan France mobile network and agricultural parcels for five different robotic scenarios. Our results show that increasing the robot's bitrate needs leads to significant additional impacts, which increase at a pace that is poorly captured by classical linear extrapolation methods. When constraining the network to the existing sites, increased bitrate needs also comes with a rapidly decreasing manageable agricultural area.

The carbon footprint of academic research has attracted growing attention in recent years, with numerous assessments conducted at the level of universities or research departments. Yet, methodological inconsistencies and small sample sizes limit comparability and hinder generalization, while concrete mitigation targets remain underdeveloped. This study draws on a national database covering about 157,000 research staff in 700 units-roughly one-third of French public research-between 2019 and 2023. Emissions are assessed across five major sources: purchases, professional travel, commuting, electricity, and heating. The dataset is used to (i) model structural determinants of research-related GHG emissions and (ii) establish reference values to guide mitigation strategies. We develop a framework to identify robust statistical models to predict average emissions levels per source. Based on staff composition, supervisory body, research domain, and geographical location, these models explain up to one-third of inter-unit variance and improve predictive accuracy by 8%-23% over baseline averages. Embedded in an online tool, these models help support the design of efficient, equitable, and realistic mitigation targets.

Attributing the carbon costs of shared ICT infrastructures to its end-users is frequently promoted as a way to encourage awareness of environmental impacts and advocate for more sustainable practices. This paper explores the intricacies of this approach by focusing on shared ICT infrastructures specifically dedicated to academic research, several of which having recently introduced carbon intensity values for their users. This scenario serves as a practical case study for examining the methodologies and challenges associated with evaluating the carbon intensity of shared ICT infrastructures. We explore the choices with their limitations, discuss the objectives behind their implementation of this type of environmental indicator and offer actionable insights. This analysis aims to contribute to the broader discussion on sustainable computing practices and the role of environmental indicators in driving meaningful change.

Digitalization appears as a lever to enhance agriculture sustainability. However, existing works on digital agriculture's own sustainability remain scarce, disregarding the environmental effects of deploying digital devices on a large scale. We propose a bottom-up method to estimate the carbon footprint of digital agriculture scenarios considering deployment of devices over a diversity of farm sizes. It is applied to two use-cases and demonstrates that digital agriculture encompasses a diversity of devices with heterogeneous carbon footprints and that more complex devices yield higher footprints not always compensated by better performances or scaling gains. By emphasizing the necessity of considering the multiplicity of devices, and the territorial distribution of farm sizes when modeling digital agriculture deployments, this study highlights the need for further exploration of the first-order effects of digital technologies in agriculture.

Dans cet article, nous présentons brièvement un enseignement optionnel de 16 h de cours intégrés de sensibilisation aux enjeux écologiques dans le numérique pour les étudiantes et étudiants en troisième année de Licence informatique à l’université de Bordeaux. Nous détaillons le projet accompagnant cet enseignement. Nous terminons enfin par une analyse des trois années d’enseignement de ce cours et décrivons des perspectives.

The assessment of energy consumption of data traffic for Internet services usually relies on energy intensity figures (in Wh/GB). In this paper, we argue against using these indicators for evaluating the evolution of energy consumption of data transmission induced by changes in Internet usage. We describe a model that estimates global impacts for different scenarios of Internet usages and technological hypothesises, and show that it can overcome some limitations of intensity indicators. We experiment the model on four use-cases: basic usage, video streaming, large downloads, and video conferencing. Results show that increasing the resolution of videos does increase the total energy consumption while misleadingly decreasing the power intensity indicator at the same time. In other words, a more efficient network does not necessarily mean less energy consumption.

Assessing the energy consumption or carbon foot-print of data distribution of video streaming services is usually carried out through energy or carbon intensity figures (in Wh or gCO2e per GB). In this paper, we first review the reasons why such approaches are likely to lead to misunderstandings and potentially to erroneous conclusions. To overcome those shortcomings, we propose a new methodology whose key idea is to consider a video streaming usage at the whole scale of a territory, and evaluate the impact of this usage on the network infrastructure. At the core of our methodology is a parametric model of a simplified network and Content Delivery Network (CDN) infrastructure, which is automatically scaled according to peak usage needs. This allows us to compare the power consumption of this infrastructure under different scenarios, ranging from a sober baseline to a generalized use of high bitrate videos. Our results show that classical efficiency indicators do not reflect the power consumption increase of more intensive Internet usage, and might even lead to misleading conclusions.

Spectral rendering is essential for the production of physically-plausible synthetic images, but requires to introduce several changes in the content generation pipeline. In particular, the authoring of spectral material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new problems. While a large panel of computer graphics methods exists to upsample a RGB color to a spectrum, they all provide a one-to-one mapping. This limits the ability to control interesting color changes such as the Usambara effect or metameric spectra. In this work, we introduce a one-to-many mapping in which we show how we can explore the set of all spectra reproducing a given input color. We apply this method to different colour changing effects such as vathochromism - the change of color with depth, and metamerism.

We present a novel depth from focus technique. Following prior work, our pipeline starts with a focal stack and an estimation of the amount of defocus as given by, for instance, the ring difference filter. To improve robustness to outliers while avoiding to rely on costly nonlinear optimizations, we propose an original scheme that linearly scans the profile over a fixed size window, searching for the best peak within each window using a linearized least-squares Laplace regression. As a post-process, depth estimates with low confidence are reconstructed though an adaptive moving least squares filter. We show how to objectively evaluate the performance of our approach by generating synthetic focal stacks from which the reconstructed depth maps can be compared to ground truth. Our results show that our method achieves higher accuracy than previous nonlinear Laplace regression technique, while being orders of magnitude faster.

The scrutiny over the carbon footprint of research and higher education has increased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies on which these estimates rely unfortunately prevents any aggregation or direct comparison. This is because carbon footprint assessments are very sensitive to key parameters (e.g., emission factors) or hypotheses (e.g., scopes). Hence, it is impossible to address fundamental questions such as: is the carbon footprint of research structurally different between disciplines? Are plane trips a major source of carbon emissions in academic research? Massive collection and curation of carbon footprint data, across a large array of research situations and disciplines, is hence an important, timely and necessary challenge to answer these questions. This paper presents a framework to collect and analyse large amounts of homogeneous research carbon emission data in a network of research entities at the national scale. It relies on an open-source web application, GES 1point5, designed to estimate the carbon footprint of a department, research lab or team in any country of the world. Importantly, GES 1point5 is also designed to aggregate all input data and corresponding GHG emissions estimates into a comprehensive database. GES 1point5 therefore enables (i) the identification of robust local or national determinants of the carbon footprint of research and (ii) the estimation of the carbon footprint of the entire research sector at national scale. A preliminary analysis of the carbon footprint of more than one hundred laboratories in France is presented to illustrate the potential of the framework. It shows that the average emissions are 479 t CO2e for a research lab and 3.6 t CO2e for an average lab member (respectively 404 and 3.1 t CO2e without accounting for the indirect radiative effects of aviation), with the current scope of GES 1point5. Availability and implementation: GES 1point5 is available online at http://labos1point5.org/ges- 1point5 and its source code can be downloaded from the GitLab platform at https://framagit.org/ labos1point5/l1p5-vuejs.

3D restitution is now a well-known tool to validate hypotheses on historical buildings that do not exist anymore. The present project takes the method a step further in order to explore the art historical topic of ornament reuse in 19th century revival architecture, particularly in Cairo. The case study is the Saint-Maurice residence, built 1875-79, for which an extensive collection of documents in varied formats, and from multiple locations and disciplines, has been conducted. The paper presents some preliminary results on the 3D restitution, the remaining open questions and the challenges they raise.

We introduce a new tool that assists artists in deforming an elastic object when it comes in intersection with a rigid one. As opposed to methods that rely on time-resolved simulations, our approach is entirely based on time-independent geometric operators. It thus restarts from scratch at every frame from a pair of objects in intersection and works in two stages: the intersected regions are first matched and a contact region is identified on the rigid object; the elastic object is then deformed to match the contact while producing plausible bulge effects with controllable volume preservation. Our direct deformation approach brings several advantages to 3D animators: it provides instant feedback, permits non-linear editing, allows for the replicability of the deformation in different settings, and grants control over exaggerated or stylized bulging effects.

With hardware tessellation, highly detailed geometric models are decomposed into patches whose tessellation factor can be specified dynamically and independently at render time to control polygon resolution. Yet, to achieve maximum efficiency, an appropriate factor needs to be selected for each patch according to its content (geometry and appearance) and the current viewpoint distance and orientation. We propose a novel patch-based error metric that addresses this problem. It summarizes both the geometrical error and the texture parametrization deviation of a simplified patch compared to the corresponding detailed surface. This metric is compact and can be efficiently evaluated on the GPU along any view direction. Furthermore, based on this metric, we devise an easy-to-implement refitting optimization that further reduces the simplification error of any decimation algorithm, and propose a new placement strategy and cost function for edge-collapses to reach the best quality/performances trade-off.

In this paper, we introduce a novel and extremely fast algorithm to compute continuous transport maps between 2D probability densities discretized on uniform grids. The core of our method is a novel iterative solver computing the L 2 optimal transport map from a grid to the uniform density in the 2D Euclidean plane. A transport map between arbitrary densities is then recovered through numerical inversion and composition. In this case, the resulting map is only approximately optimal, but it is continuous and density preserving. Our solver is derivative-free, and it converges in a few cheap iterations. We demonstrate interactive performance in various applications such as adaptive sampling, feature sensitive remeshing, and caustic design.

The area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contain a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations—not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques and provide directions for future work in surface reconstruction.

Hardware tessellation is de facto the preferred mechanism to adaptively control mesh resolution with maximal performances. However, owing to its fixed and uniform pattern, leveraging tessellation for feature-aware LOD rendering remains a challenging problem. We relax this fundamental constraint by introducing a new spatial and temporal blending mechanism of tessellation levels, which is built on top of a novel hierarchical representation of multi-resolution meshes. This mechanism allows to finely control topological changes so that vertices can be removed or added at the most appropriate location to preserve geometric features in a continuous and artifact-free manner. We then show how to extend edge-collapse based decimation methods to build feature-aware multi-resolution meshes that match the tessellation patterns. Our approach is fully compatible with current hardware tessellators and only adds a small overhead on memory consumption and tessellation cost.