Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

We introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a 3D mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well-explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically-based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute mirror images. We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques

Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

OPAL-MesoInternational audienceWe introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a 3D mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well-explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically-based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute mirror images. We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques

Multi-view Relighting using a Geometry-Aware Network

International audienceWe propose the first learning-based algorithm that can relight images in a plausible and controllable manner given multiple views of an outdoor scene. In particular, we introduce a geometry-aware neural network that utilizes multiple geometry cues (normal maps, specular direction, etc.) and source and target shadow masks computed from a noisy proxy geometry obtained by multi-view stereo. Our model is a three-stage pipeline: two subnetworks refine the source and target shadow masks, and a third performs the final relighting. Furthermore, we introduce a novel representation for the shadow masks, which we call RGB shadow images. They reproject the colors from all views into the shadowed pixels and enable our network to cope with inacuraccies in the proxy and the non-locality of the shadow casting interactions. Acquiring large-scale multi-view relighting datasets for real scenes is challenging, so we train our network on photorealistic synthetic data. At train time, we also compute a noisy stereo-based geometric proxy, this time from the synthetic renderings. This allows us to bridge the gap between the real and synthetic domains. Our model generalizes well to real scenes. It can alter the illumination of drone footage, image-based renderings, textured mesh reconstructions, and even internet photo collections

Shape parametrization of bio-mechanical finite element models based on medical images

The main objective of this study is to combine the statistical shape analysis with a morphing procedure in order to generate shape-parametric finite element models of tissues and organs and to explore the reliability and the limitations of this approach when applied to databases of real medical images. As classical statistical shape models are not always adapted to the morphing procedure, a new registration method was developed in order to maximize the morphing efficiency. The method was compared to the traditional iterative thin plate spline (iTPS). Two data sets of 33 proximal femora shapes and 385 liver shapes were used for the comparison. The principal component analysis was used to get the principal morphing modes. In terms of anatomical shape reconstruction (evaluated through the criteria of generalization, compactness and specificity), our approach compared fairly well to the iTPS method, while performing remarkably better in terms of mesh quality, since it was less prone to generate invalid meshes in the interior. This was particularly true in the liver case. Such methodology offers a potential application for the generation of automated finite element (FE) models from medical images. Parametrized anatomical models can also be used to assess the influence of inter-patient variability on the biomechanical response of the tissues. Indeed, thanks to the shape parametrization the user would easily have access to a valid FE model for any shape belonging to the parameters subspace

Gastrointestinal biomarkers and their association with feeding in the first five days of pediatric critical illness

OBJECTIVES: Predicting the patients' tolerance to enteral nutrition (EN) would help clinicians optimize individual nutritional intake. This study investigated the course of several gastrointestinal (GI) biomarkers and their association with EN advancement (ENA) longitudinally during pediatric intensive care unit (PICU) admission.METHODS: This is a secondary analysis of the PEPaNIC RCT. EN was started early and increased gradually. The cholecystokinin (CCK), leptin, glucagon, intestinal fatty acid-binding protein 2 (I-FABP2), and citrulline plasma concentrations were measured upon PICU admission, day three and day five. ENA was defined as kcal EN provided as % of predicted resting energy expenditure (pREE). The course of the biomarkers and ENA was examined in patients with samples on all time points using Friedman and Wilcoxon signed-rank tests. The association of ENA with the biomarkers was examined using a two-part mixed-effects model with data of the complete population, adjusted for possible confounders.RESULTS: For 172 patients, median age 8.6 years (first quartile (Q1); third quartile (Q3): 4.2; 13.4), samples were available, of which 55 had samples on all time points. The median ENA was 0 (0; 0) on admission, 14.5 (0.0; 43.8) on day 3 and 28.0 (7.6; 94.8) on day 5. During PICU stay, CCK and I-FABP2 concentrations decreased significantly, whereas glucagon concentrations increased significantly, and leptin and citrulline remained stable. None of the biomarkers was longitudinally associated with ENA.CONCLUSIONS: Based on the current evidence, CCK, leptin, glucagon, I-FABP2, and citrulline appear to have no added value in predicting ENA in the first five days of pediatric critical illness.</p

TESS Hunt for Young and Maturing Exoplanets (THYME). X. A Two-planet System in the 210 Myr MELANGE-5 Association

Young (&lt;500 Myr) planets are critical to studying how planets form and evolve. Among these young planetary systems, multiplanet configurations are particularly useful, as they provide a means to control for variables within a system. Here, we report the discovery and characterization of a young planetary system, TOI-1224. We show that the planet host resides within a young population we denote as MELANGE-5. By employing a range of age-dating methods—isochrone fitting, lithium abundance analysis, gyrochronology, and Gaia excess variability—we estimate the age of MELANGE-5 to be 210 ± 27 Myr. MELANGE-5 is situated in close proximity to previously identified younger (80–110 Myr) associations, Crius 221 and Theia 424/Volans-Carina, motivating further work to map out the group boundaries. In addition to a planet candidate detected by the TESS pipeline and alerted as a TESS object of interest, TOI-1224 b, we identify a second planet, TOI-1224 c, using custom search tools optimized for young stars (Notch and LOCoR). We find that the planets are 2.10 ± 0.09 R⊕ and 2.88 ± 0.10 R⊕ and orbit their host star every 4.18 and 17.95 days, respectively. With their bright (K = 9.1 mag), small (R* = 0.44 R⊙), and cool (Teff = 3326 K) host star, these planets represent excellent candidates for atmospheric characterization with JWST

TESS Hunt for Young and Maturing Exoplanets (THYME). X. A Two-planet System in the 210 Myr MELANGE-5 Association

Young (&lt;500 Myr) planets are critical to studying how planets form and evolve. Among these young planetary systems, multiplanet configurations are particularly useful, as they provide a means to control for variables within a system. Here, we report the discovery and characterization of a young planetary system, TOI-1224. We show that the planet host resides within a young population we denote as MELANGE-5. By employing a range of age-dating methods—isochrone fitting, lithium abundance analysis, gyrochronology, and Gaia excess variability—we estimate the age of MELANGE-5 to be 210 ± 27 Myr. MELANGE-5 is situated in close proximity to previously identified younger (80–110 Myr) associations, Crius 221 and Theia 424/Volans-Carina, motivating further work to map out the group boundaries. In addition to a planet candidate detected by the TESS pipeline and alerted as a TESS object of interest, TOI-1224 b, we identify a second planet, TOI-1224 c, using custom search tools optimized for young stars (Notch and LOCoR). We find that the planets are 2.10 ± 0.09 R⊕ and 2.88 ± 0.10 R⊕ and orbit their host star every 4.18 and 17.95 days, respectively. With their bright (K = 9.1 mag), small (R* = 0.44 R⊙), and cool (Teff = 3326 K) host star, these planets represent excellent candidates for atmospheric characterization with JWST

Workshop to review and progress the reported lists of eu msfd descriptor 3 ( (WKD3LISTS)

info:eu-repo/semantics/publishedVersio

TOI-3235 b: a transiting giant planet around an M4 dwarf star

We present the discovery of TOI-3235 b, a short-period Jupiter orbiting an M-dwarf with a stellar mass close to the critical mass at which stars transition from partially to fully convective. TOI-3235 b was first identified as a candidate from TESS photometry, and confirmed with radial velocities from ESPRESSO, and ground-based photometry from HATSouth, MEarth-South, TRAPPIST-South, LCOGT, and ExTrA. We find that the planet has a mass of $\mathrm{0.665\pm0.025\,M_J}$ and a radius of $\mathrm{1.017\pm0.044\,R_J}$. It orbits close to its host star, with an orbital period of $\mathrm{2.5926\,d}$, but has an equilibrium temperature of $\mathrm{\approx 604 \, K}$, well below the expected threshold for radius inflation of hot Jupiters. The host star has a mass of $\mathrm{0.3939\pm0.0030\,M_\odot}$, a radius of $\mathrm{0.3697\pm0.0018\,R_\odot}$, an effective temperature of $\mathrm{3389 \, K}$, and a J-band magnitude of $\mathrm{11.706\pm0.025}$. Current planet formation models do not predict the existence of gas giants such as TOI-3235 b around such low-mass stars. With a high transmission spectroscopy metric, TOI-3235 b is one of the best-suited giants orbiting M-dwarfs for atmospheric characterization.Comment: 15 pages, 4 figures. Accepted for publication in APJ