Multi-View Dynamic Shape Refinement Using Local Temporal Integration

International audienceWe consider 4D shape reconstructions in multi-view environments and investigate how to exploit temporal redundancy for precision refinement. In addition to being beneficial to many dynamic multi-view scenarios this also enables larger scenes where such increased precision can compensate for the reduced spatial resolution per image frame. With precision and scalability in mind, we propose a symmetric (non-causal) local time-window geometric integration scheme over temporal sequences, where shape reconstructions are refined framewise by warping local and reliable geometric regions of neighboring frames to them. This is in contrast to recent comparable approaches targeting a different context with more compact scenes and real-time applications. These usually use a single dense volumetric update space or geometric template, which they causally track and update globally frame by frame, with limitations in scalability for larger scenes and in topology and precision with a template based strategy. Our templateless and local approach is a first step towards temporal shape super-resolution. We show that it improves reconstruction accuracy by considering multiple frames. To this purpose, and in addition to real data examples, we introduce a multi-camera synthetic dataset that provides ground-truth data for mid-scale dynamic scenes

Super Résolution Temporelle de Formes Multi-Vues

International audienceNous considérons le problème de super résolution temporelle de formes, par l'utilisation de multiples observations d'un même modèle déformé. Sans pertes de généralité, nous nous concentrons plus particulièrement au scénario multi-camera moyenne échelle, c'est à dire des scènes dynamiques, pouvant contenir plusieurs sujets. Ce contexte favorise l'utilisation de caméras couleur, mais nécessite une méthode de reconstruction robuste aux inconsistances photométriques. Dans ce but, nous proposons une nouvelle approche, spécialement dédiée à ce contexte moyenne échelle, utilisant des descripteurs et des schémas de votes adaptés. Cette méthode est étendue à la dimension temporelle de manière à améliorer les reconstructions à chaque instant, en exploitant la redondance des informations dans le temps. Pour cela, les informations photométriques fiables sont accumulées dans le temps à l'aide de champs de déformations combinés à une stratégie de croissance de région. Nous démontrons l'amélioration des reconstructions apportée par notre approche à l'aide de séquences multi-camera synthétiques

Apprentissage de la Cohérence Photométrique pour la Reconstruction de Formes Multi-Vues

International audienceWith the rise of augmented and virtual reality, estimating accurate shapes from multi-view RGB images is becoming an important task in computer vision. The dominant strategy employed for that purpose in the recent years relies on depth maps estimation followed by depth fusion, as depth maps prove to be efficient in recovering local surface details. Motivated by recent success of convolutional neural networks, we take this strategy a step further and present a novel solution for depth map estimation which consists in sweeping a volume along projected rays from a camera, and inferring surface presence probability at a point, seen by an arbitrary number of cameras. A strong motivation behind this work is to study the ability of learning based features to outperform traditional 2D features when estimating depth from multi-view cues. Especially with real life dynamic scenes, containing multiple moving subjects with complex surface details, scenarios where previous image based MVS methods fail to recover accurate details. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recovering surface details on dynamic scenes that are not visible to traditional 2D feature based methods. In addition, our evaluation also includes a comparison to existing reconstruction pipelines on the standard evaluation dataset we used to train our network with, showing that our solution performs on par or better than these approaches.L'essor des technologies de réalité virtuelle et augmentée s'accompagne d'un besoin accru de contenus appropriés à ces technologies et à leurs méthodes de visualisation. En particulier, la capacité à produire des contenus réels visualisables en 3D devient prépondérante. Nous considérons dans cet article le problème de la reconstruction de scènes 3D dynamiques à partir d'images couleurs. Nous intéressons tout particulièrement à la possibilité de bénéficier des réseaux de neurones convolutifs dans ce processus de reconstruction pour l'améliorer de manière effective. Les méthodes les plus récentes de reconstruction multi-vues estiment des cartes de profondeur par vue et fusionnent ensuite ces cartes dans une forme implicite 3D. Une étape clé de ces méthodes réside dans l'estimation des cartes de profondeurs. Cette étape est traditionnellement effectuée par la recherche de correspondances multi-vues à l'aide de critères de photo-cohérence. Nous proposons ici d'apprendre cette fonction de photo-cohérence sur des exemples au lieu de la définir à travers la corrélation de descripteurs photométriques, comme c'est le cas dans la plupart des méthodes actuelles. L'intuition est que la corrélation de descripteurs d'images est intrinsèquement contrainte et limitée, et que les réseaux profonds ont la capacité d'apprendre des configurations plus larges. Nos résultats sur des données réelles démontrent que cela est le cas. Entraîné sur un jeu de données statiques standard, les réseaux de convolution nous permettent de récupérer des détails sur une forme en mouvement que les descripteurs d'images classiques ne peuvent extraire. Les évaluations comparatives sur ces données standards sont par ailleurs favorables à la méthode que nous proposons

The prion or the related Shadoo protein is required for early mouse embryogenesis

AbstractThe prion protein PrP has a key role in transmissible spongiform encephalopathies but its biological function remains largely unknown. Recently, a related protein, Shadoo, was discovered. Its biological properties and brain distribution partially overlap that of PrP. We report that the Shadoo-encoding gene knockdown in PrP-knockout mouse embryos results in a lethal phenotype, occurring between E8 and E11, not observed on the wild-type genetic background. It reveals that these two proteins play a shared, crucial role in mammalian embryogenesis, explaining the lack of severe phenotype in PrP-knockout mammals, an appreciable step towards deciphering the biological role of this protein family

The Efficiency of Mechanized Mineral Processing Techniques to Recover Tin and Tantalum Ores. Case Study: Nyamatete Concession, Rwanda

Mining activities have resulted in a large volume of tailings containing a certain proportion of lost minerals, making them a potential reprocessing opportunity, and information on tailings and mineral reprocessing is often scarce. In this study, a conceptual framework was established and used to produce meaningful information and knowledge from the tailings of the Nyamatete mine at HABATU Mining Company Limited (an ASM in Rwanda). Tailings particles were investigated based on the observed lithology to determine their distribution over the tailing dams, and a site-specific sampling approach and procedure were established. Sieving the collected samples, particle size analysis, and chemical analysis using XRF were chosen as methods for tailings characterization. Raw materials such as SnO2, minor Ta2O5, and Nb2O5and elements of environmental importance such as Mn, Co, and as were observed but in small quantities are largely predominant in quartz vein, have been discovered in Nyamatete tailings. The comminution and gravity separation by mechanical reprocessing facilities improved Cassiterite recovery by 43.9 %compared to the artisanal processing method. SnO2recovery of 19.4% and 29.9% with grades of 63.224% and 76.6% were obtained in pegmatite and quartz tailings respectively. According to a scoping study, the Habatu tailings have an appropriate grade, the valuable content occurs in a recoverable grain-size range, and the total ore amount scales with the required input for the reprocessing equipment. Tailings reprocessing with mechanized reprocessing equipment is advantageous because it improves efficiency while also treating the material with previously unknown quantities of ore, thereby significantly increasing the total recovery of processed Run of Mine

Parsonage-Turner Syndrome as a Rare Extrahepatic Complication of Hepatitis E Infection

Parsonage-Turner syndrome, also known as neuralgic amyotrophy, is a rare disorder characterized by painful clinical manifestations mainly involving the upper limbs. This syndrome seems to be triggered, among other factors, by some viral infections, although its pathophysiology remains unclear. Moreover, it has rarely been related to hepatitis E virus infection. We report the case of a 33-year-old man who was diagnosed with Parsonage-Turner syndrome following acute hepatitis E infection

Changes in the Economy and Ecology at Proposed Lake Sites in the Salt River Basin, Kentucky, During Early Construction of the Dam for Taylorsville Lake

This is an extension of the work reported in Project numbers B-005-KY, B-016-KY, and B-022-KY that extended from 1 July 1968 through 30 June 1972. Permanent collecting stations have been established at 67 sites throughout the Salt River, Beech Fork, and Chaplin River drainages. Turbidities increases quickly as flow and runoff increase, and subside quickly when the rain stops. Suspended solids range up to 1,700 mg/l in high turbidities and vary considerably as a result of local spates. Water chemistry generally reflects the limestone nature of the substrate and physico-chemical characteristics of a typically healthy limestone stream. Bottom organisms are abundant and diverse, more than 300 different benthic organisms have been identified to date

4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements

This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain different actors wearing different outfits while performing different motions in each outfit. In this way, the dataset can be seen as a cube of data containing 4D motion sequences along 3 axes with identity, outfit and motion. This rich dataset has numerous potential applications for the processing and creation of digital humans, e.g. augmented reality, avatar creation and virtual try on. 4DHumanOutfit is released for research purposes at https://kinovis.inria.fr/4dhumanoutfit/. In addition to image data and 4D reconstructions, the dataset includes reference solutions for each axis. We present independent baselines along each axis that demonstrate the value of these reference solutions for evaluation tasks

The Lantern Vol. 39, No. 1, Fall 1972

• A Journey Into Darkness • September 5, 1972 • Atlantic Taperecorder • Aftermath • Linda • Sweet Baby Jane • The Court of the Ebony Clown • The Cosmic Band • Poem to the Dreamer • Dawn • Too Bad Life Isn\u27t • Incident at Tiffany\u27s • Sonnet • Infinitas • Podiatry • 2 and 4a • Autistic Autumn • I Walk Alone • Eyes---and They Were Emptyhttps://digitalcommons.ursinus.edu/lantern/1101/thumbnail.jp

The Lantern Vol. 39, No. 2, Spring 1973

• Days of Rain • Reflections On Clifton, New Jersey • Interlude • Window Scene • Eh! • Odyssey of Malcolm • Tuna on Toast • The Second Avenue Bus • Salutation of the Dawn • So Say Something • Mood • Moriarty\u27s Lament • I\u27ve Been a Lonely Gypsy • Change • Cool Ray • The Thinker • A Southern Sunsethttps://digitalcommons.ursinus.edu/lantern/1102/thumbnail.jp