DeepSketchHair: Deep Sketch-based 3D Hair Modeling

We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally and locally. The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field. Our system also supports hair editing with additional sketches in new views. This is enabled by another deep neural network, V2VNet, which updates the 3D vector field with respect to the new sketches. All the three networks are trained with synthetic data generated from a 3D hairstyle database. We demonstrate the effectiveness and expressiveness of our tool using a variety of hairstyles and also compare our method with prior art

DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth. The synthesized realistic depth can then be used to train task-specific networks facilitating label transfer from the synthetic domain. Unlike existing image synthesis pipelines, where geometries are mostly ignored, we treat geometries carried by the depth scans based on their own existence. We propose differential contrastive learning that explicitly enforces the underlying geometric properties to be invariant regarding the real variations been learned. The resulting depth synthesis method is task-agnostic, and we demonstrate the effectiveness of the proposed synthesis method by extensive evaluations on real-world geometric reasoning tasks. The networks trained with the depth synthesized by our method consistently achieve better performance across a wide range of tasks than state of the art, and can even surpass the networks supervised with full real-world annotations when slightly fine-tuned, showing good transferability.Comment: Accepted by International Conference on Robotics and Automation (ICRA) 2022 and RA-L 202

GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

In this paper, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising methods that exploit hand-crafted or voxel-based representations for feature learning, our method explores the structure of a triangular mesh itself and introduces a graph representation followed by graph convolution operations in the dual space of triangles. We show such a graph representation naturally captures the geometry features while being lightweight for both training and inference. To facilitate effective feature learning, our network exploits both static and dynamic edge convolutions, which allow us to learn information from both the explicit mesh structure and potential implicit relations among unconnected neighbors. To better approximate an unknown noise function, we introduce a cascaded optimization paradigm to progressively regress the noise-free facet normals with multiple GCNs. GCN-Denoiser achieves the new state-of-the-art results in multiple noise datasets, including CAD models often containing sharp features and raw scan models with real noise captured from different devices. We also create a new dataset called PrintData containing 20 real scans with their corresponding ground-truth meshes for the research community. Our code and data are available in https://github.com/Jhonve/GCN-Denoiser.Comment: Accepted by ACM Transactions on Graphics 202

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size

Domain Adaptation on Point Clouds via Geometry-Aware Implicits

As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet unsolved issue for learning on point cloud is that point clouds of the same object can have significant geometric variations if generated using different procedures or captured using different sensors. These inconsistencies induce domain gaps such that neural networks trained on one domain may fail to generalize on others. A typical technique to reduce the domain gap is to perform adversarial training so that point clouds in the feature space can align. However, adversarial training is easy to fall into degenerated local minima, resulting in negative adaptation gains. Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot. First, the geometric information in the point clouds is preserved through the implicit representations for downstream tasks. More importantly, the domain-specific variations can be effectively learned away in the implicit space. We also propose an adaptive strategy to compute unsigned distance fields for arbitrary point clouds due to the lack of shape models in practice. When combined with a task loss, the proposed outperforms state-of-the-art unsupervised domain adaptation methods that rely on adversarial domain alignment and more complicated self-supervised tasks. Our method is evaluated on both PointDA-10 and GraspNet datasets. The code and trained models will be publicly available

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size

Staple Food Preference and Obesity Phenotypes: The Regional Ethnic Cohort Study in Northwest China

Staple food preference vary in populations, but evidence of its associations with obesity phenotypes are limited. Using baseline data (n = 105,840) of the Regional Ethnic Cohort Study in Northwest China, staple food preference was defined according to the intake frequency of rice and wheat. Overall and specifically abdominal fat accumulation were determined by excessive body fat percentage and waist circumference. Logistic regression and equal frequency substitution methods were used to evaluate the associations. We observed rice preference (consuming rice more frequently than wheat; 7.84% for men and 8.28% for women) was associated with a lower risk of excessive body fat (OR, 0.743; 95%CI, 0.669–0.826) and central obesity (OR, 0.886; 95%CI, 0.807–0.971) in men; and with lower risk of central obesity (OR, 0.898; 95%CI, 0.836–0.964) in women, compared with their wheat preference counterparties. Furthermore, similar but stronger inverse associations were observed in participants with normal body mass index. Wheat-to-rice (5 times/week) reallocations were associated with a 36.5% lower risk of normal-weight obesity in men and a 20.5% lower risk of normal-weight central obesity in women. Our data suggest that, compared with wheat, rice preference could be associated with lower odds ratios of certain obesity phenotypes in the Northwest Chinese population

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software