Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

Contribution of Piezo2 to endothelium-dependent pain.

BackgroundWe evaluated the role of a mechanically-gated ion channel, Piezo2, in mechanical stimulation-induced enhancement of hyperalgesia produced by the pronociceptive vasoactive mediator endothelin-1, an innocuous mechanical stimulus-induced enhancement of hyperalgesia that is vascular endothelial cell dependent. We also evaluated its role in a preclinical model of a vascular endothelial cell dependent painful peripheral neuropathy.ResultsThe local administration of oligodeoxynucleotides antisense to Piezo2 mRNA, at the site of nociceptive testing in the rat's hind paw, but not intrathecally at the central terminal of the nociceptor, prevented innocuous stimulus-induced enhancement of hyperalgesia produced by endothelin-1 (100 ng). The mechanical hyperalgesia induced by oxaliplatin (2 mg/kg. i.v.), which was inhibited by impairing endothelial cell function, was similarly attenuated by local injection of the Piezo2 antisense. Polymerase chain reaction analysis demonstrated for the first time the presence of Piezo2 mRNA in endothelial cells.ConclusionsThese results support the hypothesis that Piezo2 is a mechano-transducer in the endothelial cell where it contributes to stimulus-dependent hyperalgesia, and a model of chemotherapy-induced painful peripheral neuropathy

High-order myopic coronagraphic phase diversity (COFFEE) for wave-front control in high-contrast imaging systems

The estimation and compensation of quasi-static aberrations is mandatory to reach the ultimate performance of high-contrast imaging systems. COFFEE is a focal plane wave-front sensing method that consists in the extension of phase diversity to high-contrast imaging systems. Based on a Bayesian approach, it estimates the quasi-static aberrations from two focal plane images recorded from the scientific camera itself. In this paper, we present COFFEE's extension which allows an estimation of low and high order aberrations with nanometric precision for any coronagraphic device. The performance is evaluated by realistic simulations, performed in the SPHERE instrument framework. We develop a myopic estimation that allows us to take into account an imperfect knowledge on the used diversity phase. Lastly, we evaluate COFFEE's performance in a compensation process, to optimize the contrast on the detector, and show it allows one to reach the 10^-6 contrast required by SPHERE at a few resolution elements from the star. Notably, we present a non-linear energy minimization method which can be used to reach very high contrast levels (better than 10^-7 in a SPHERE-like context)Comment: Accepted in Optics Expres

Detecting topological phases in the square-octagon lattice with statistical methods

Electronic systems living on Archimedean lattices such as kagome and square-octagon networks are presently being intensively discussed for the possible realization of topological insulating phases. Coining the most interesting electronic topological states in an unbiased way is however not straightforward due to the large parameter space of possible Hamiltonians. Here we focus on the square-octagon lattice and explore via a recently developed statistical learning method the possible topological phases that can manifest when the Fermi level of the system lies at a high-order van Hove singularity. For this purpose, we analyze large data sets of randomized tight-binding Hamiltonians labeled with the corresponding topological index and, through feature engineering, we identify the observables that are associated with non-trivial topological phases. Our analysis provides a recipe to construct tight-binding Hamiltonians for the insulating phases with Chern number $\pm$1.Comment: 9 pages, 6 figure

A cross-sectional study of long-term satisfaction after surgery for congenital syndactyly:does skin grafting influence satisfaction?

Syndactyly correction without skin grafting is advocated because it prevents graft-related complications and donor site morbidity. In this cross-sectional study, we compared satisfaction among patients who underwent correction with and without skin grafting to determine preference based on subjective and objective parameters. Retrospective chart analysis was performed among 27 patients (49 webs) who were seen at follow-up after a median follow-up period of 7.4 years, at which the Patient and Observer Scar Assessment Scale, the Withey score and a satisfaction survey were used. Notably, there were no significant differences in complication rates or observer rated scar scores. Although the need for an additional surgical procedure was higher after skin grafting, patient-rated satisfaction scores were similar irrespective of the use of grafting. Our data suggest that corrections can best be performed without skin grafts if seeking to minimize the need for an additional procedure, but that the use of skin grafts does not appear to affect patient satisfaction. Level of evidence: IV

Ranking the importance of genetic factors by variable-selection confidence sets

The widespread use of generalized linear models in case–control genetic studies has helped to identify many disease-associated risk factors typically defined as DNA variants, or single-nucleotide polymorphisms (SNPs). Up to now, most literature has focused on selecting a unique best subset of SNPs based on some statistical perspective. When the noise is large compared with the signal, however, multiple biological paths are often found to be supported by a given data set. We address the ambiguity related to SNP selection by constructing a list of models—called a variable-selection confidence set (VSCS)—which contains the collection of all well-supported SNP combinations at a user-specified confidence level. The VSCS extends the familiar notion of confidence intervals in the variable-selection setting and provides the practitioner with new tools aiding the variable-selection activity beyond trusting a single model. On the basis of the VSCS, we consider natural graphical and numerical statistics measuring the inclusion importance of an SNP based on its frequency in the most parsimonious VSCS models. This work is motivated by available case–control genetic data on age-related macular degeneration, which is a widespread disease and leading cause of loss of vision. © 2019 Royal Statistical Societ