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"title": "2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)",
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"title": "Research on Adaptive Feedrate Planning of NURBS Curves for CNC System",
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"abstract": "In CNC machining, non-uniform rational B-spline (NURBS) curve is commonly used to describe the tool path for machining complex curves and surfaces. In order to improve the machining accuracy of NURBS curve, it is necessary to control the feedrate under geometric and kinematic constraints throughout the machining process. This paper proposes a segmental adaptive feedrate planning algorithm with low allowable feedrate at critical sub-curves and high allowable feedrate at regular sub-curves, which makes a balance between interpolation precision and efficiency. To solve the remaining distance problem caused by the contradiction between continuous feedrate planning and periodic interpolation as well as the nonlinear relationship between arc length and parameters, this paper calculates step-length using displacement-time function on every interpolation period and proposes a step-length correcting algorithm at the intersection of two adjacent sub-curves. The simulation results show that smooth motion under the chord-error and kinematic constraints can be obtained within curves with sharp curvature variation throughout the interpolation process, which validate the advantages of the proposed algorithm.",
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"content": "In CNC machining, non-uniform rational B-spline (NURBS) curve is commonly used to describe the tool path for machining complex curves and surfaces. In order to improve the machining accuracy of NURBS curve, it is necessary to control the feedrate under geometric and kinematic constraints throughout the machining process. This paper proposes a segmental adaptive feedrate planning algorithm with low allowable feedrate at critical sub-curves and high allowable feedrate at regular sub-curves, which makes a balance between interpolation precision and efficiency. To solve the remaining distance problem caused by the contradiction between continuous feedrate planning and periodic interpolation as well as the nonlinear relationship between arc length and parameters, this paper calculates step-length using displacement-time function on every interpolation period and proposes a step-length correcting algorithm at the intersection of two adjacent sub-curves. The simulation results show that smooth motion under the chord-error and kinematic constraints can be obtained within curves with sharp curvature variation throughout the interpolation process, which validate the advantages of the proposed algorithm.",
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"abstract": "In the past forty years, the high-performance computing (HPC) community has been developing powerful and rigorous tools for predicting the performance of supercomputers from log traces. In this paper, we transform one of these approaches previously used for predicting idle resources in high-end clusters into a method for capturing extreme climate events in geographical locations of interest. Our method uses an analysis based on empirical cumulative distribution functions (ECDFs) to benchmark and model occurrences of climate events including extreme temperature and precipitation. The method comprises two phases: a learning phase and a prediction phase. The learning phase applies the ECDF-based empirical analysis to historical climate data in order to identify suitable modeling and forecasting windows, both given in years. The prediction phase applies the modeling window to the most recent climate data in order to estimate the likelihood that given portions of the region of interest can experience extreme climate events in the forecasting window. The research is the first of its kind to extend HPC performance modeling techniques to study extreme climate events.",
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"title": "HPC infrastructure to support the next-generation ARM facility data operations",
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"abstract": "The Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is establishing an adaptive data services and operations architecture in support of the Next-Generation ARM Facility as explained in its Decadal Vision. In this paper, we describe the capabilities of the ARM Data Center (ADC) and the upcoming high-performance computing infrastructure in support of this Next-Generation ARM Facility.",
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"content": "The Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is establishing an adaptive data services and operations architecture in support of the Next-Generation ARM Facility as explained in its Decadal Vision. In this paper, we describe the capabilities of the ARM Data Center (ADC) and the upcoming high-performance computing infrastructure in support of this Next-Generation ARM Facility.",
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"affiliation": "ARM Climate Research Facility Data Center, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN USA",
"fullName": "Giri Prakash",
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"affiliation": "ARM Climate Research Facility Data Center, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN USA",
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"affiliation": "ARM Climate Research Facility Data Center, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN USA",
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"affiliation": "ARM Climate Research Facility Data Center, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN USA",
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"affiliation": "ARM Climate Research Facility Data Center, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN USA",
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"abstract": "Characterizing the underlying data generation processes for extreme values in complex spatiotemporal dynamical systems, and developing predictive insights on these extremes, can improve our fundamental understanding of fluid dynamics including turbulence and eddies. The translational benefits are expected to span disciplines such as earth or atmospheric sciences and space sciences all the way to water resources engineering or electrical power grids and the propagation of diseases in humans or information in social media. However, each discipline may offer unique challenges. One challenge in understanding and projecting mean change and extremes patterns in climate, and translating to climate adaptation, is the inherent natural variability of the climate system, which dominates in the stakeholder-relevant 0-30 year near-term. The nonlinear dynamical climate system exhibits high sensitivity to initial conditions. Climate model simulations attempt to capture the inherent natural variability in climate systems through multiple initial condition ensembles. In addition, the variability resulting from gaps in our process understanding are encapsulated through multi model ensembles based on parametric and structural differences in models, while uncertainties in emissions trajectories are described via what-if scenario ensembles. While extreme value theory has been found useful for studying climate extremes, the multiplier of ensembles may disproportionately increase the uncertainty in projections. Here we examine the hypothesis that initial condition ensembles, which are generated from identical dynamical simulations, can be examined collectively to narrow the uncertainty in our assessments of precipitation extremes under climate variability. Our findings may be directional for climate studies and potentially relevant for related disciplines.",
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"abstract": "Kilometer-scale ensemble simulations are expected to significantly boost and impact weather and climate predictions in the future. However, these simulations will only be enabled by exascale compute power and corresponding data capacity. In the following, we discuss a European effort in terms of the e-infrastructure Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE). ESiWACE provides infrastructural means to prepare the weather and climate communities for simulations at the exascale. We give an overview of several ESiWACE infrastructure components and discuss their role in reaching the goal of kilometer-scale ensemble predictions. We particularly review the outcomes of the ESiWACE demonstrators, that is community-driven kilometer-scale models that have been developed throughout the last years.",
"abstracts": [
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"content": "Kilometer-scale ensemble simulations are expected to significantly boost and impact weather and climate predictions in the future. However, these simulations will only be enabled by exascale compute power and corresponding data capacity. In the following, we discuss a European effort in terms of the e-infrastructure Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE). ESiWACE provides infrastructural means to prepare the weather and climate communities for simulations at the exascale. We give an overview of several ESiWACE infrastructure components and discuss their role in reaching the goal of kilometer-scale ensemble predictions. We particularly review the outcomes of the ESiWACE demonstrators, that is community-driven kilometer-scale models that have been developed throughout the last years.",
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"Community Driven Kilometer Scale Models",
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"Kilometer Scale Ensemble Predictions",
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"Climate Communities",
"Infrastructural Means",
"European Effort",
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"abstract": "In this work we present a new method for mesh denoising that uses an operator based on the Quadric Error Metric. This operator is able to estimate the local shape of the surface for each vertex, despite severe noise condition, distinguishing corners, edges and smooth regions in order to best adjust the vertex geometry to recover piecewise smoothing while preserving sharp features. Our method results in a simple algorithm for mesh denoising that can also be used to enhance sharp features present in the surface corrupted by noise. A frequency response analysis is also presented in order to evaluate the characteristics of this operator in the frequency spectrum of the mesh.",
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"abstract": "Recent advances in genotyping technology have facilitated the use of genome-wide association studies (GWAS) to successfully identify genetic variants that are associated with common complex traits. Following the successes in identification of single variants, joint identification including gene-gene interaction has been studied vigorously and produced many novel results. However, most genome-wide association studies have been conducted by focusing on one trait of interest for identifying genetic variants associated with common complex traits. Since many complex diseases having severe influences on the public health are pleiotropic, simple univariate analysis focusing on a single trait does not well detect full genetic architecture of complex diseases. For example, hyperlipidemia is diagnosed by four multiple traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). Surprisingly, however, only few studies handle multiple traits simultaneously so far. Therefore, in order to improve power and reflect biological association more expansively, we investigate a multivariate approach which considers multiple traits simultaneously. Especially for the gene-gene interaction analysis for the multiple traits, we extend original multifactor dimensionality reduction (MDR) to handle multiple traits. We then demonstrate its superiority to univariate analysis through simulation studies. We confirm that the multivariate approach provides more stable and precise accuracy measures compared to univariate analysis. We applied the multivariate MDR approach to a GWA dataset of 8,842 Korean individuals and detected genetic variants associated with hypertension traits using systolic blood pressure (SBP) and Diastolic blood pressure (DBP).",
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"content": "Recent advances in genotyping technology have facilitated the use of genome-wide association studies (GWAS) to successfully identify genetic variants that are associated with common complex traits. Following the successes in identification of single variants, joint identification including gene-gene interaction has been studied vigorously and produced many novel results. However, most genome-wide association studies have been conducted by focusing on one trait of interest for identifying genetic variants associated with common complex traits. Since many complex diseases having severe influences on the public health are pleiotropic, simple univariate analysis focusing on a single trait does not well detect full genetic architecture of complex diseases. For example, hyperlipidemia is diagnosed by four multiple traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). Surprisingly, however, only few studies handle multiple traits simultaneously so far. Therefore, in order to improve power and reflect biological association more expansively, we investigate a multivariate approach which considers multiple traits simultaneously. Especially for the gene-gene interaction analysis for the multiple traits, we extend original multifactor dimensionality reduction (MDR) to handle multiple traits. We then demonstrate its superiority to univariate analysis through simulation studies. We confirm that the multivariate approach provides more stable and precise accuracy measures compared to univariate analysis. We applied the multivariate MDR approach to a GWA dataset of 8,842 Korean individuals and detected genetic variants associated with hypertension traits using systolic blood pressure (SBP) and Diastolic blood pressure (DBP).",
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"normalizedAbstract": "Recent advances in genotyping technology have facilitated the use of genome-wide association studies (GWAS) to successfully identify genetic variants that are associated with common complex traits. Following the successes in identification of single variants, joint identification including gene-gene interaction has been studied vigorously and produced many novel results. However, most genome-wide association studies have been conducted by focusing on one trait of interest for identifying genetic variants associated with common complex traits. Since many complex diseases having severe influences on the public health are pleiotropic, simple univariate analysis focusing on a single trait does not well detect full genetic architecture of complex diseases. For example, hyperlipidemia is diagnosed by four multiple traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). Surprisingly, however, only few studies handle multiple traits simultaneously so far. Therefore, in order to improve power and reflect biological association more expansively, we investigate a multivariate approach which considers multiple traits simultaneously. Especially for the gene-gene interaction analysis for the multiple traits, we extend original multifactor dimensionality reduction (MDR) to handle multiple traits. We then demonstrate its superiority to univariate analysis through simulation studies. We confirm that the multivariate approach provides more stable and precise accuracy measures compared to univariate analysis. We applied the multivariate MDR approach to a GWA dataset of 8,842 Korean individuals and detected genetic variants associated with hypertension traits using systolic blood pressure (SBP) and Diastolic blood pressure (DBP).",
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"abstract": "MicroRNA(miRNA) plays an important role in regulating the expression of target mRNAs. The deregulation of microRNAs appears to associate with various diseases. Recently, researchers focus on making use of various biological properties to identify the associations between microRNAs and diseases so as to provide helpful information for disease therapies. Accumulate evidences have shown that the inter- and intra-relationships of microRNAs, diseases, environment factors and genes contribute to correctly detect candidate microRNA-disease associations. However, there lack of methods that can comprehensively make use of the advantage of these relationships. In this work, we construct four separate biological networks, that are microRNA functional similarity network(MFN), disease semantic similarity network(DSN), environmental factor chemical structure similarity network(ESN) and gene-gene functional similarity network( GSN). After that, an unbalanced four random walking method, namely FourRW is implemented on the four networks, which not only can flexibly infer information from different levels of neighbors in the four networks, but also realizes the information transfer between different networks. The results of experiment show that our method achieves better prediction performance than the other state-of-the-art methods.",
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"content": "MicroRNA(miRNA) plays an important role in regulating the expression of target mRNAs. The deregulation of microRNAs appears to associate with various diseases. Recently, researchers focus on making use of various biological properties to identify the associations between microRNAs and diseases so as to provide helpful information for disease therapies. Accumulate evidences have shown that the inter- and intra-relationships of microRNAs, diseases, environment factors and genes contribute to correctly detect candidate microRNA-disease associations. However, there lack of methods that can comprehensively make use of the advantage of these relationships. In this work, we construct four separate biological networks, that are microRNA functional similarity network(MFN), disease semantic similarity network(DSN), environmental factor chemical structure similarity network(ESN) and gene-gene functional similarity network( GSN). After that, an unbalanced four random walking method, namely FourRW is implemented on the four networks, which not only can flexibly infer information from different levels of neighbors in the four networks, but also realizes the information transfer between different networks. The results of experiment show that our method achieves better prediction performance than the other state-of-the-art methods.",
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"affiliation": "School of Information Science and Engineering, Central South University, Changsha, 410083, China",
"fullName": "Jianxin Wang",
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"affiliation": "Department of Computer Science, Georgia State University, Atlanta, 30302-4110, USA",
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"abstract": "Nutritional Genomics is demanding computing models and technological platforms in order to support acquisition, storage, management and presentation of all the information generated coming from heterogeneous sources: genotypes, environmental factors (diet and other life-style factors) and phenotypes (intermediate and final phenotypes). Our aim is to build a biomedical ontology in order to modelling gene*environment interactions on intermediate phenotypes by means of formalising and integrating genomic, environmental and phenotypic data, in the field of research on Nutritional Genomics applied to cardiovascular diseases and associated phenotypes. This ontology is part of a Health Information System based on Web engineering technologies, architectures and services, and integrating several data types to support Nutritional Genomics research in cardiovascular diseases.",
"abstracts": [
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"content": "Nutritional Genomics is demanding computing models and technological platforms in order to support acquisition, storage, management and presentation of all the information generated coming from heterogeneous sources: genotypes, environmental factors (diet and other life-style factors) and phenotypes (intermediate and final phenotypes). Our aim is to build a biomedical ontology in order to modelling gene*environment interactions on intermediate phenotypes by means of formalising and integrating genomic, environmental and phenotypic data, in the field of research on Nutritional Genomics applied to cardiovascular diseases and associated phenotypes. This ontology is part of a Health Information System based on Web engineering technologies, architectures and services, and integrating several data types to support Nutritional Genomics research in cardiovascular diseases.",
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"affiliation": "Department of Computer Languages and Systems, Universitat Jaume I, Castellon, Spain",
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"affiliation": "Department of Computer Languages and Systems, Universitat Jaume I, Castellon, Spain",
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"title": "Leveraging Integrative Knowledge Graphs to Improve Health Information Access for Rare Diseases",
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"abstract": "The increasing availability of data in translational science affords an unprecedented opportunity to improve health information access for rare diseases. To that end, we leverage our recent effort in building a comprehensive knowledge graph to provide a holistic view of rare diseases so as to empower patients and their families. We illustrate this holistic view through a preview of our upcoming updates to our rare disease information portal GARD.",
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"content": "The increasing availability of data in translational science affords an unprecedented opportunity to improve health information access for rare diseases. To that end, we leverage our recent effort in building a comprehensive knowledge graph to provide a holistic view of rare diseases so as to empower patients and their families. We illustrate this holistic view through a preview of our upcoming updates to our rare disease information portal GARD.",
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"content": "LiDAR (Light Detection And Ranging) acquisition is a widespread method for measuring urban scenes, be it a small town neighborhood or an entire city. It is even more interesting when this acquisition is coupled with a collection of pictures registered with the data, permitting to recover the color information of the points. Yet, this added color can be perturbed by shadows that are very dependent on the sun direction and weather conditions during the acquisition. In this paper, we focus on the problem of automatically detecting and correcting the shadows from the LiDAR data by exploiting both the images and the point set laser reflectance. Building on the observation that shadow boundaries are characterized by both a significant color change and a stable laser reflectance, we propose to first detect shadow boundaries in the point set and then segment ground shadows using graph cuts in the image. Finally using a simplified illumination model we correct the shadows directly on the colored point sets. This joint exploitation of both the laser point set and the images renders our approach robust and efficient, avoiding user interaction.",
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"abstract": "Vision-based automotive driver assistance systems have to cope with complex outdoor illumination conditions. Many applications for vehicle detection and tracking consider shadows caused by the sunlight as distracting effects and try to computationally compensate or disregard them. In this paper we suggest a shape-from-shadow approach which uses cast shadows as additional supporting information for vehicle model refinement and tracking. To take the position of the sun into account we only use sensor systems that mid-range vehicles are normally equipped with. Analysing shadows is a suitable method to support rear-view based vehicle tracking. A vehicle's shadow turned out to be a strong feature since it is possible to track a vehicle solely based on its shadow. Another benefit is that we are able to set up and refine three-dimensional shape models of vehicles driving ahead in the same lane. In selected situations it is possible to visually track two vehicles driving on the same lane one after another.",
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"abstract": "Shadows, the common phenomena in most outdoor scenes, are illuminated by diffuse skylight whereas shaded from direct sunlight. Generally shadows take place in sunny weather when the spectral power distributions (SPD) of sunlight, skylight, and daylight show strong regularity: they principally vary with sun angles. In this paper, we first deduce that the pixel values of a surface illuminated by skylight (in shadow region) and by daylight (in non-shadow region) have a linear relationship, and the linearity is independent of surface reflectance and holds in each color channel. We then use six simulated images that contain 1995 surfaces and two real captured images to test the linearity. The results validate the linearity. Based on the deduced linear relationship, we develop three shadow processing applications include intrinsic image deriving, shadow verification, and shadow removal. The results of the applications demonstrate that the linear relationship have practical values.",
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"abstract": "Shadows are a frequent occurrence, but they cannot be infallibly recognized until a scene's geometry and lighting are known. We present a number of cues which together strongly suggest the identification of a shadow and which can be examined with low cost. The techniques are: a color image segmentation method that recovers single material surfaces as single image regions irregardless of the surface partially in shadow, a method to recover the penumbra and umbra of shadow; a method for determining whether some object could be obstructing a light source. The last cue requires the examination of well understood shadows in the scene. Our observer is equipped with an extendable probe for casting its own shadows. Actively obtained shadows allow the observer to experimentally determine the location of the light sources in the scene. The system has been tested both indoors and out.",
"abstracts": [
{
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"content": "Shadows are a frequent occurrence, but they cannot be infallibly recognized until a scene's geometry and lighting are known. We present a number of cues which together strongly suggest the identification of a shadow and which can be examined with low cost. The techniques are: a color image segmentation method that recovers single material surfaces as single image regions irregardless of the surface partially in shadow, a method to recover the penumbra and umbra of shadow; a method for determining whether some object could be obstructing a light source. The last cue requires the examination of well understood shadows in the scene. Our observer is equipped with an extendable probe for casting its own shadows. Actively obtained shadows allow the observer to experimentally determine the location of the light sources in the scene. The system has been tested both indoors and out.",
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"normalizedAbstract": "Shadows are a frequent occurrence, but they cannot be infallibly recognized until a scene's geometry and lighting are known. We present a number of cues which together strongly suggest the identification of a shadow and which can be examined with low cost. The techniques are: a color image segmentation method that recovers single material surfaces as single image regions irregardless of the surface partially in shadow, a method to recover the penumbra and umbra of shadow; a method for determining whether some object could be obstructing a light source. The last cue requires the examination of well understood shadows in the scene. Our observer is equipped with an extendable probe for casting its own shadows. Actively obtained shadows allow the observer to experimentally determine the location of the light sources in the scene. The system has been tested both indoors and out.",
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"abstract": "Differentiable rendering has received increasing interest for image-based inverse problems. It can benefit traditional optimization-based solutions to inverse problems, but also allows for self-supervision of learning-based approaches for which training data with ground truth annotation is hard to obtain. However, existing differentiable renderers either do not model visibility of the light sources from the different points in the scene, responsible for shadows in the images, or are very slow which makes it difficult to train deep architectures over thousands of iterations. To this end, we propose an accurate yet efficient approach for differentiable visibility and soft shadow computation. Our approach is based on the spherical harmonics approximations of the scene illumination and visibility, where the occluding surface is approximated with spheres. This allows for a significantly more efficient shadow computation compared to methods based on ray tracing. As our formulation is differentiable, it can be used to solve inverse problems such as texture, illumination, rigid pose, and geometric deformation recovery from images using analysis-by-synthesis optimization.",
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"content": "Differentiable rendering has received increasing interest for image-based inverse problems. It can benefit traditional optimization-based solutions to inverse problems, but also allows for self-supervision of learning-based approaches for which training data with ground truth annotation is hard to obtain. However, existing differentiable renderers either do not model visibility of the light sources from the different points in the scene, responsible for shadows in the images, or are very slow which makes it difficult to train deep architectures over thousands of iterations. To this end, we propose an accurate yet efficient approach for differentiable visibility and soft shadow computation. Our approach is based on the spherical harmonics approximations of the scene illumination and visibility, where the occluding surface is approximated with spheres. This allows for a significantly more efficient shadow computation compared to methods based on ray tracing. As our formulation is differentiable, it can be used to solve inverse problems such as texture, illumination, rigid pose, and geometric deformation recovery from images using analysis-by-synthesis optimization.",
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"normalizedAbstract": "Differentiable rendering has received increasing interest for image-based inverse problems. It can benefit traditional optimization-based solutions to inverse problems, but also allows for self-supervision of learning-based approaches for which training data with ground truth annotation is hard to obtain. However, existing differentiable renderers either do not model visibility of the light sources from the different points in the scene, responsible for shadows in the images, or are very slow which makes it difficult to train deep architectures over thousands of iterations. To this end, we propose an accurate yet efficient approach for differentiable visibility and soft shadow computation. Our approach is based on the spherical harmonics approximations of the scene illumination and visibility, where the occluding surface is approximated with spheres. This allows for a significantly more efficient shadow computation compared to methods based on ray tracing. As our formulation is differentiable, it can be used to solve inverse problems such as texture, illumination, rigid pose, and geometric deformation recovery from images using analysis-by-synthesis optimization.",
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"title": "Shadow Inducers: Inconspicuous Highlights for Casting Virtual Shadows on OST-HMDs",
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"abstract": "Virtual shadows provide important cues to determine the positional relationship between virtual objects and a real scene. However, it' s difficult to render shadows in the real scene on optical see-through head-mounted displays without occlusion-capability. In the previous work, we cast virtual shadows not with physical light attenuation but with brightness induction caused by virtual objects, referred to as shadow inducers, which surround the shadow area to gradually amplify the intensity of the real scene pattern [4]. However, because the shadow inducer was prepared beforehand, the shape of the shadow is constant, the real scene shadowing is limited to a flat surface, and a large of viewpoint change is impossible. In this demonstration, we propose a method to generate shadow inducers in real time that can change the shape of virtual objects and the viewpoint of users. In this method, depending on the appearance of shadows, the surrounding luminance is gradually amplified with the difference of gaussian (DOG) representing characteristics of human vision. Users can observe shadows of moving and deforming virtual objects on a real tabletop and other non-planar objects.",
"abstracts": [
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"content": "Virtual shadows provide important cues to determine the positional relationship between virtual objects and a real scene. However, it' s difficult to render shadows in the real scene on optical see-through head-mounted displays without occlusion-capability. In the previous work, we cast virtual shadows not with physical light attenuation but with brightness induction caused by virtual objects, referred to as shadow inducers, which surround the shadow area to gradually amplify the intensity of the real scene pattern [4]. However, because the shadow inducer was prepared beforehand, the shape of the shadow is constant, the real scene shadowing is limited to a flat surface, and a large of viewpoint change is impossible. In this demonstration, we propose a method to generate shadow inducers in real time that can change the shape of virtual objects and the viewpoint of users. In this method, depending on the appearance of shadows, the surrounding luminance is gradually amplified with the difference of gaussian (DOG) representing characteristics of human vision. Users can observe shadows of moving and deforming virtual objects on a real tabletop and other non-planar objects.",
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"abstract": "Conventional cameras capture images with limited depth of field and no depth information. Camera systems have been proposed that enable additional depth information to be captured with the image. These systems reduce the resolution of the captured image or result in reduced sensitivity of the lens. We demonstrate a camera that is able to capture extended depth of field images together with depth information at each single frame while requiring minimal impact on the physical design of the camera or its performance. In this paper we show results with a camera for mobile devices, but this technology (named dual aperture to recall the major change in the camera model) can be applied with even greater effect in larger form factor cameras.",
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"abstract": "The fast development of time-of-flight (ToF) cameras in recent years enables capture of high frame-rate 3D depth maps of moving objects. However, the resolution of depth map captured by ToF is rather limited, and thus it cannot be directly used to build a high quality 3D model. In order to handle this problem, we propose a novel joint example-based depth map super-resolution method, which converts a low resolution depth map to a high resolution depth map, using a registered high resolution color image as a reference. Different from previous depth map SR methods without training stage, we learn a mapping function from a set of training samples and enhance the resolution of the depth map via sparse coding algorithm. We further use a reconstruction constraint to make object edges sharper. Experimental results show that our method outperforms state-of-the-art methods for depth map super-resolution.",
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"abstract": "The emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is necessary. With the purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, in this work we propose a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining low-resolution intensity images and depth maps in an optimization process, our methodology creates new images and depth maps of higher resolution and, at the same time, minimizes issues related with the absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images and depth maps acquired with low resolution, commercially available sensors.",
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"content": "The emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is necessary. With the purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, in this work we propose a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining low-resolution intensity images and depth maps in an optimization process, our methodology creates new images and depth maps of higher resolution and, at the same time, minimizes issues related with the absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images and depth maps acquired with low resolution, commercially available sensors.",
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"title": "Recovering spectral reflectance under commonly available lighting conditions",
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"abstract": "Recovering the spectral reflectance of a scene is important for scene understanding. Previous approaches use either specialized filters or controlled illumination where the extra hardware prevents many practical applications. In this paper, we propose a method that accurately recovers spectral reflectance from two images taken with conventional consumer cameras under commonly available lighting conditions, such as daylight at different times over a day, camera flash and ambient light, and fluorescent and tungsten light. Our approach does not require camera spectral sensitivities or the spectra of the illumination, which makes it easy to implement for a variety of practical applications. Based on noise analysis, we also derive theoretical predictors that answer: (1) which two lighting conditions lead to the most accurate spectral recovery overall, and (2) for two given lighting conditions, which spectral reflectance is more likely to be estimated accurately. We implement the method on a variety of cameras from high-end DSLRs to cellphone cameras, and apply the recovered spectral reflectance for several applications such as fine art reproduction, fruit identification, and material classification. Both simulation and experimental results demonstrate the effectiveness of the proposed method.",
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"content": "Recovering the spectral reflectance of a scene is important for scene understanding. Previous approaches use either specialized filters or controlled illumination where the extra hardware prevents many practical applications. In this paper, we propose a method that accurately recovers spectral reflectance from two images taken with conventional consumer cameras under commonly available lighting conditions, such as daylight at different times over a day, camera flash and ambient light, and fluorescent and tungsten light. Our approach does not require camera spectral sensitivities or the spectra of the illumination, which makes it easy to implement for a variety of practical applications. Based on noise analysis, we also derive theoretical predictors that answer: (1) which two lighting conditions lead to the most accurate spectral recovery overall, and (2) for two given lighting conditions, which spectral reflectance is more likely to be estimated accurately. We implement the method on a variety of cameras from high-end DSLRs to cellphone cameras, and apply the recovered spectral reflectance for several applications such as fine art reproduction, fruit identification, and material classification. Both simulation and experimental results demonstrate the effectiveness of the proposed method.",
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"abstract": "We introduce Light Collages — a lighting design system for effective visualization based on principles of human perception. Artists and illustrators enhance perception of features with lighting that is locally consistent and globally inconsistent. Inspired by these techniques, we design the placement of light sources to convey a greater sense of realism and better perception of shape with globally inconsistent lighting. Our algorithm segments the objects into local surface patches and uses a number of perceptual heuristics, such as highlights, shadows, and silhouettes, to enhance the perception of shape. We show our results on scientific and sculptured datasets.",
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"affiliation": "University of Maryland at College Park",
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"affiliation": "University of Maryland at College Park",
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"affiliation": "University of Maryland at College Park",
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"content": "Reconstruction of the shape and motion of humans from RGB-D is a challenging problem, receiving much attention in recent years. Recent approaches for full-body reconstruction use a statistic shape model, which is built upon accurate full-body scans of people in skin-tight clothes, to complete invisible parts due to occlusion. Such a statistic model may still be fit to an RGB-D measurement with loose clothes but cannot describe its deformations, such as clothing wrinkles. Observed surfaces may be reconstructed precisely from actual measurements, while we have no cues for unobserved surfaces. For full-body reconstruction with loose clothes, we propose to use lower dimensional embeddings of texture and deformation referred to as eigen-texturing and eigen-deformation, to reproduce views of even unobserved surfaces. Provided a full-body reconstruction from a sequence of partial measurements as 3D meshes, the texture and deformation of each triangle are then embedded using eigen-decomposition. Combined with neural-network-based coefficient regression, our method synthesizes the texture and deformation from arbitrary viewpoints. We evaluate our method using simulated data and visually demonstrate how our method works on real data.",
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"affiliation": "Kyushu University, Japan",
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"abstract": "We present Neural Generalized Implicit Functions (Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses. Existing methods have relied on template-based representations of the human body (or clothing). However such models usually have fixed and limited resolutions, require difficult data pre-processing steps and cannot be used with complex clothing. We draw inspiration from template-based methods, which factorize motion into articulation and nonrigid deformation, but generalize this concept for implicit shape learning to obtain a more flexible model. We learn to map every point in the space to a canonical space, where a learned deformation field is applied to model non-rigid effects, before evaluating the signed distance field. Our formulation allows the learning of complex and non-rigid deformations of clothing and soft tissue, without computing a template registration as it is common with current approaches. Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations. Moreover, the model can generalize to new poses. We evaluate our method on a variety of characters from different public datasets in diverse clothing styles and show significant improvements over baseline methods, quantitatively and qualitatively. We also extend our model to multiple shape setting. To stimulate further research, we will make the model, code and data publicly available at [1].",
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"abstract": "Currently it requires an artist to create 3D human avatars with realistic clothing that can move naturally. Despite progress on 3D scanning and modeling of human bodies, there is still no technology that can easily turn a static scan into an animatable avatar. Automating the creation of such avatars would enable many applications in games, social networking, animation, and AR/VR to name a few. The key problem is one of representation. Standard 3D meshes are widely used in modeling the minimally-clothed body but do not readily capture the complex topology of clothing. Recent interest has shifted to implicit surface models for this task but they are computationally heavy and lack compatibility with existing 3D tools. What is needed is a 3D representation that can capture varied topology at high resolution and that can be learned from data. We argue that this representation has been with us all along — the point cloud. Point clouds have properties of both implicit and explicit representations that we exploit to model 3D garment geometry on a human body. We train a neural network with a novel local clothing geometric feature to represent the shape of different outfits. The network is trained from 3D point clouds of many types of clothing, on many bodies, in many poses, and learns to model pose-dependent clothing deformations. The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically. Our model demonstrates superior quantitative and qualitative results in both multi-outfit modeling and unseen outfit animation. The code is available for research purposes at https://qianlim.github.io/POP.",
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"content": "Currently it requires an artist to create 3D human avatars with realistic clothing that can move naturally. Despite progress on 3D scanning and modeling of human bodies, there is still no technology that can easily turn a static scan into an animatable avatar. Automating the creation of such avatars would enable many applications in games, social networking, animation, and AR/VR to name a few. The key problem is one of representation. Standard 3D meshes are widely used in modeling the minimally-clothed body but do not readily capture the complex topology of clothing. Recent interest has shifted to implicit surface models for this task but they are computationally heavy and lack compatibility with existing 3D tools. What is needed is a 3D representation that can capture varied topology at high resolution and that can be learned from data. We argue that this representation has been with us all along — the point cloud. Point clouds have properties of both implicit and explicit representations that we exploit to model 3D garment geometry on a human body. We train a neural network with a novel local clothing geometric feature to represent the shape of different outfits. The network is trained from 3D point clouds of many types of clothing, on many bodies, in many poses, and learns to model pose-dependent clothing deformations. The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically. Our model demonstrates superior quantitative and qualitative results in both multi-outfit modeling and unseen outfit animation. The code is available for research purposes at https://qianlim.github.io/POP.",
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"affiliation": "Max Planck Institute for Intelligent Systems,Tübingen,Germany",
"fullName": "Qianli Ma",
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