InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data

Myosin motor Myo1c and its receptor NEMO/IKK-γ promote TNF-α–induced serine307 phosphorylation of IRS-1

Tumor necrosis factor-α (TNF-α) signaling through the IκB kinase (IKK) complex attenuates insulin action via the phosphorylation of insulin receptor substrate 1 (IRS-1) at Ser307. However, the precise molecular mechanism by which the IKK complex phosphorylates IRS-1 is unknown. In this study, we report nuclear factor κB essential modulator (NEMO)/IKK-γ subunit accumulation in membrane ruffles followed by an interaction with IRS-1. This intracellular trafficking of NEMO requires insulin, an intact actin cytoskeletal network, and the motor protein Myo1c. Increased Myo1c expression enhanced the NEMO–IRS-1 interaction, which is essential for TNF-α– induced phosphorylation of Ser307–IRS-1. In contrast, dominant inhibitory Myo1c cargo domain expression diminished this interaction and inhibited IRS-1 phosphorylation. NEMO expression also enhanced TNF-α–induced Ser307–IRS-1 phosphorylation and inhibited glucose uptake. In contrast, a deletion mutant of NEMO lacking the IKK-β–binding domain or silencing NEMO blocked the TNF-α signal. Thus, motor protein Myo1c and its receptor protein NEMO act cooperatively to form the IKK–IRS-1 complex and function in TNF-α–induced insulin resistance

Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?

International audienceAccurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

International audienceWe seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data

Molecular identification of 1-Cys peroxiredoxin and anthocyanidin/flavonol 3-O-galactosyltransferase from proanthocyanidin-rich young fruits of persimmon (Diospyros kaki Thunb.)

Fruits of persimmon (Diospyros kaki Thunb.) accumulate large amounts of proanthocyanidins (PAs) in the early stages of development. Astringent (A)-type fruits remain rich in soluble PAs even after they reach full-mature stage, whereas non-astringent (NA)-type fruits lose these compounds before full maturation. As a first step to elucidate the mechanism of PA accumulation in this non-model species, we used suppression subtractive hybridization to identify transcripts accumulating differently in young fruits of A- and NA-type. Interestingly, only a few clones involved in PA biosynthesis were identified in A–NA libraries. Represented by multiple clones were those encoding a novel 1-Cys peroxiredoxin and a new member of family 1 glycosyltransferases. Quantitative RT-PCR analyses confirmed correlation of the amount of PAs and accumulation of transcripts encoding these proteins in young persimmon fruits. Furthermore, the new family 1 glycosyltransferase was produced in Escherichia coli and shown to efficiently catalyze galactosylation at 3-hydroxyl groups of several anthocyanidins and flavonols. These findings suggest a complex mechanism of PA accumulation in persimmon fruits