bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distortion‐Free Displacement Mapping
Authors: Zirr Tobias , Ritschel Tobias 
Published: November 2019

AbstractDisplacement mapping is routinely used to add geometric details in a fast and easy‐to‐control way, both in offline rendering as well as recently in interactive applications such as games. However, it went largely unnoticed (with the exception of McGuire and Whitson [MW08]) that, when applying displacement mapping to a surface with a low‐distortion parametrization, this parametrization is distorted as the geometry was changed by the displacement mapping. Typical resulting artifacts are “rubber band”‐like distortion patterns in areas of strong displacement change where a small isotropic area in texture space is mapped to a large anisotropic area in world space. We describe a fast, fully GPU‐based two‐step procedure to resolve this problem. First, a correction deformation is computed from the displacement map. Second, two variants to apply this correction when computing displacement mapping are proposed. The first variant is backward‐compatible and can resolve the artifact in any rendering pipeline without modifying it and without requiring additional computation at render time, but only works for bijective parametrizations. The second variant works for more general parametrizations, but requires to modify the rendering code and incurs a very small computational overhead.
Pencil Drawing Video Rendering Using Convolutional Networks
Authors: Yan Dingkun , Sheng Yun , Mao Xiaoyang 
Published: November 2019

AbstractTraditional pencil drawing rendering algorithms when applied to video may suffer from temporal inconsistency and shower‐door effect due to the stochastic noise models employed. This paper attempts to resolve these problems with deep learning. Recently, many research endeavors have demonstrated that feed‐forward Convolutional Neural Networks (CNNs) are capable of using a reference image to stylize a whole video sequence while removing the shower‐door effect in video style transfer applications. Compared with video style transfer, pencil drawing video is more sensitive to the inconsistency of texture and requires a stronger expression of pencil hatching. Thus, in this paper we develop an approach by combining a latest Line Integral Convolution (LIC) based method, specializing in realistically simulating pencil drawing images, with a new feed‐forward CNN that can eliminate the shower‐door effect successfully. Taking advantage of optical flow, we adopt a feature‐map‐level temporal loss function and propose a new framework to avoid the temporal inconsistency between consecutive frames, enhancing the visual impression of pencil strokes and tone. Experimental comparisons with the existing feed‐forward CNNs have demonstrated that our method can generate temporally more stable and visually more pleasant pencil drawing video results in a faster manner.
Active Scene Understanding via Online Semantic Reconstruction
Authors: Zheng Lintao , Zhu Chenyang , Zhang Jiazhao , Zhao Hang , Huang Hui , Niessner Matthias , Xu Kai 
Published: November 2019

AbstractWe propose a novel approach to robot‐operated active understanding of unknown indoor scenes, based on online RGBD reconstruction with semantic segmentation. In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene. Our algorithm is built on top of a volumetric depth fusion framework and performs real‐time voxel‐based semantic labeling over the online reconstructed volume. The robot is guided by an online estimated discrete viewing score field (VSF) parameterized over the 3D space of 2D location and azimuth rotation. VSF stores for each grid the score of the corresponding view, which measures how much it reduces the uncertainty (entropy) of both geometric reconstruction and semantic labeling. Based on VSF, we select the next best views (NBV) as the target for each time step. We then jointly optimize the traverse path and camera trajectory between two adjacent NBVs, through maximizing the integral viewing score (information gain) along path and trajectory. Through extensive evaluation, we show that our method achieves efficient and accurate online scene parsing during exploratory scanning.
Learning Explicit Smoothing Kernels for Joint Image Filtering
Authors: Fang Xiaonan , Wang Miao , Shamir Ariel , Hu Shi‐Min 
Published: November 2019

AbstractSmoothing noises while preserving strong edges in images is an important problem in image processing. Image smoothing filters can be either explicit (based on local weighted average) or implicit (based on global optimization). Implicit methods are usually time‐consuming and cannot be applied to joint image filtering tasks, i.e., leveraging the structural information of a guidance image to filter a target image. Previous deep learning based image smoothing filters are all implicit and unavailable for joint filtering. In this paper, we propose to learn explicit guidance feature maps as well as offset maps from the guidance image and smoothing parameter that can be utilized to smooth the input itself or to filter images in other target domains. We design a deep convolutional neural network consisting of a fully‐convolution block for guidance and offset maps extraction together with a stacked spatially varying deformable convolution block for joint image filtering. Our models can approximate several representative image smoothing filters with high accuracy comparable to state‐of‐the‐art methods, and serve as general tools for other joint image filtering tasks, such as color interpolation, depth map upsampling, saliency map upsampling, flash/non‐flash image denoising and RGB/NIR image denoising.
Dual Illumination Estimation for Robust Exposure Correction
Authors: Zhang Qing , Nie Yongwei , Zheng Wei‐Shi 
Published: November 2019

AbstractExposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high‐quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under‐ and over‐exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the under‐and over‐exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi‐exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well‐exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state‐of‐the‐art methods and popular automatic exposure correction tools.
Specular Highlight Removal for Real‐world Images
Authors: Fu Gang , Zhang Qing , Song Chengfang , Lin Qifeng , Xiao Chunxia 
Published: November 2019

AbstractRemoving specular highlight in an image is a fundamental research problem in computer vision and computer graphics. While various methods have been proposed, they typically do not work well for real‐world images due to the presence of rich textures, complex materials, hard shadows, occlusions and color illumination, etc. In this paper, we present a novel specular highlight removal method for real‐world images. Our approach is based on two observations of the real‐world images: (i) the specular highlight is often small in size and sparse in distribution; (ii) the remaining diffuse image can be represented by linear combination of a small number of basis colors with the sparse encoding coefficients. Based on the two observations, we design an optimization framework for simultaneously estimating the diffuse and specular highlight images from a single image. Specifically, we recover the diffuse components of those regions with specular highlight by encouraging the encoding coefficients sparseness using L0 norm. Moreover, the encoding coefficients and specular highlight are also subject to the non‐negativity according to the additive color mixing theory and the illumination definition, respectively. Extensive experiments have been performed on a variety of images to validate the effectiveness of the proposed method and its superiority over the previous methods.
Computing Surface PolyCube‐Maps by Constrained Voxelization
Authors: Yang Yang , Fu Xiao‐Ming , Liu Ligang 
Published: November 2019

AbstractWe present a novel method to compute bijective PolyCube‐maps with low isometric distortion. Given a surface and its pre‐axis‐aligned shape that is not an exact PolyCube shape, the algorithm contains two steps: (i) construct a PolyCube shape to approximate the pre‐axis‐aligned shape; and (ii) generate a bijective, low isometric distortion mapping between the constructed PolyCube shape and the input surface. The PolyCube construction is formulated as a constrained optimization problem, where the objective is the number of corners in the constructed PolyCube, and the constraint is to bound the approximation error between the constructed PolyCube and the input pre‐axis‐aligned shape while ensuring topological validity. A novel erasing‐and‐filling solver is proposed to solve this challenging problem. Centeral to the algorithm for computing bijective PolyCube‐maps is a quad mesh optimization process that projects the constructed PolyCube onto the input surface with high‐quality quads. We demonstrate the efficacy of our algorithm on a data set containing 300 closed meshes. Compared to state‐of‐the‐art methods, our method achieves higher practical robustness and lower mapping distortion.
Shadow Inpainting and Removal Using Generative Adversarial Networks with Slice Convolutions
Authors: Wei Jinjiang , Long Chengjiang , Zou Hua , Xiao Chunxia 
Published: November 2019

AbstractIn this paper, we propose a two‐stage top‐down and bottom‐up Generative Adversarial Networks (TBGANs) for shadow inpainting and removal which uses a novel top‐down encoder and a bottom‐up decoder with slice convolutions. These slice convolutions can effectively extract and restore the long‐range spatial information for either down‐sampling or up‐sampling. Different from the previous shadow removal methods based on deep learning, we propose to inpaint shadow to handle the possible dark shadows to achieve a coarse shadow‐removal image at the first stage, and then further recover the details and enhance the color and texture details with a non‐local block to explore both local and global inter‐dependencies of pixels at the second stage. With such a two‐stage coarse‐to‐fine processing, the overall effect of shadow removal is greatly improved, and the effect of color retention in non‐shaded areas is significant. By comparing with a variety of mainstream shadow removal methods, we demonstrate that our proposed method outperforms the state‐of‐the‐art methods.
High Dynamic Range Point Clouds for Real‐Time Relighting
Authors: Sabbadin Manuele , Palma Gianpaolo , Banterle Francesco , Boubekeur Tamy , Cignoni Paolo 
Published: November 2019

AbstractAcquired 3D point clouds make possible quick modeling of virtual scenes from the real world. With modern 3D capture pipelines, each point sample often comes with additional attributes such as normal vector and color response. Although rendering and processing such data has been extensively studied, little attention has been devoted using the light transport hidden in the recorded per‐sample color response to relight virtual objects in visual effects (VFX) look‐dev or augmented reality (AR) scenarios. Typically, standard relighting environment exploits global environment maps together with a collection of local light probes to reflect the light mood of the real scene on the virtual object. We propose instead a unified spatial approximation of the radiance and visibility relationships present in the scene, in the form of a colored point cloud. To do so, our method relies on two core components: High Dynamic Range (HDR) expansion and real‐time Point‐Based Global Illumination (PBGI). First, since an acquired color point cloud typically comes in Low Dynamic Range (LDR) format, we boost it using a single HDR photo exemplar of the captured scene that can cover part of it. We perform this expansion efficiently by first expanding the dynamic range of a set of renderings of the point cloud and then projecting these renderings on the original cloud. At this stage, we propagate the expansion to the regions not covered by the renderings or with low‐quality dynamic range by solving a Poisson system. Then, at rendering time, we use the resulting HDR point cloud to relight virtual objects, providing a diffuse model of the indirect illumination propagated by the environment. To do so, we design a PBGI algorithm that exploits the GPU's geometry shader stage as well as a new mipmapping operator, tailored for G‐buffers, to achieve real‐time performances. As a result, our method can effectively relight virtual objects exhibiting diffuse and glossy physically‐based materials in real time. Furthermore, it accounts for the spatial embedding of the object within the 3D environment. We evaluate our approach on manufactured scenes to assess the error introduced at every step from the perfect ground truth. We also report experiments with real captured data, covering a range of capture technologies, from active scanning to multiview stereo reconstruction.
Deep Video‐Based Performance Synthesis from Sparse Multi‐View Capture
Authors: Chen Mingjia , Wang Changbo , Liu Ligang 
Published: November 2019

AbstractWe present a deep learning based technique that enables novel‐view videos of human performances to be synthesized from sparse multi‐view captures. While performance capturing from a sparse set of videos has received significant attention, there has been relatively less progress which is about non‐rigid objects (e.g., human bodies). The rich articulation modes of human body make it rather challenging to synthesize and interpolate the model well. To address this problem, we propose a novel deep learning based framework that directly predicts novel‐view videos of human performances without explicit 3D reconstruction. Our method is a composition of two steps: novel‐view prediction and detail enhancement. We first learn a novel deep generative query network for view prediction. We synthesize novel‐view performances from a sparse set of just five or less camera videos. Then, we use a new generative adversarial network to enhance fine‐scale details of the first step results. This opens up the possibility of high‐quality low‐cost video‐based performance synthesis, which is gaining popularity for VA and AR applications. We demonstrate a variety of promising results, where our method is able to synthesis more robust and accurate performances than existing state‐of‐the‐art approaches when only sparse views are available.
Appearance Flow Completion for Novel View Synthesis
Authors: Le Hoang , Liu Feng 
Published: November 2019

AbstractNovel view synthesis from sparse and unstructured input views faces challenges like the difficulty with dense 3D reconstruction and large occlusion. This paper addresses these problems by estimating proper appearance flows from the target to input views to warp and blend the input views. Our method first estimates a sparse set 3D scene points using an off‐the‐shelf 3D reconstruction method and calculates sparse flows from the target to input views. Our method then performs appearance flow completion to estimate the dense flows from the corresponding sparse ones. Specifically, we design a deep fully convolutional neural network that takes sparse flows and input views as input and outputs the dense flows. Furthermore, we estimate the optical flows between input views as references to guide the estimation of dense flows between the target view and input views. Besides the dense flows, our network also estimates the masks to blend multiple warped inputs to render the target view. Experiments on the KITTI benchmark show that our method can generate high quality novel views from sparse and unstructured input views.
Selecting texture resolution using a task‐specific visibility metric
Authors: Wolski K. , Giunchi D. , Kinuwaki S. , Didyk P. , Myszkowski K. , Steed A. , Mantiuk R. K. 
Published: November 2019

AbstractIn real‐time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non‐trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per‐pixel annotated visibility maps which serve as the training data for application‐specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game‐engine where we optimize the resolution of various textures, such as albedo and normal maps.
Histologic analysis following grafting of damaged extraction sockets using deproteinized bovine or porcine bone mineral: A randomized clinical trial
Authors: Koo Tae‐Hwan , Song Young Woo , Cha Jae‐Kook , Jung Ui‐Won , Kim Chang‐Sung , Lee Jung‐Seok 
Published: November 2019

AbstractObjectivesThis study histologically analyzed biopsy samples obtained from sites of damaged extraction socket grafting using deproteinized bovine bone mineral (DBBM) or deproteinized porcine bone mineral (DPBM) with coverage by a collagen membrane.Material and methodsOne hundred patients participated in this randomized controlled clinical trial of extraction socket grafts performed in cases of periodontally compromised teeth. All participants were blinded to their group allocations, and each material was grafted with coverage by collagen membranes after extraction of the tooth and removal of granulation tissue. At implant placement at 4 months, a biopsy was harvested at the implant site using a trephine was analyzed histologically.ResultsEighty‐five biopsy samples were acquired, of which 81 were finally included in the histologic analysis (42 in DBBM and 39 in DPBM group). Both DBBM and DPBM groups showed comparable proportions of residual biomaterial (12.37 ± 5.67% and 12.21 ± 5.75%, respectively), newly formed bone (15.07 ± 10.52% and 18.47 ± 11.47%, respectively), and nonmineralized tissue (72.56 ± 10.07% and 71.55 ± 15.47%, respectively). There were no significant differences in these histologic parameters between the two groups with different biomaterials.ConclusionComparable histologic bone formation was found in both socket grafted groups with DBBM or DPBM covered by collagen membranes in periodontally damaged extraction sockets. However, a wide variation in new bone formation was found after 4 months of postsurgical healing and a tendency of higher new bone formation was shown at damaged sockets that had an intact unilateral residual wall regardless of buccal or lingual side.
Better biodiversity accounting is needed to prevent bioperversity and maximize co‐benefits from savanna burning
Authors: Corey Ben , Andersen Alan N. , Legge Sarah , Woinarski John C. Z. , Radford Ian J. , Perry Justin J. 
Published: November 2019

AbstractStrategies for mitigating climate change through altered land management practices can provide win–win outcomes for the environment and the economy. Emissions trading for greenhouse gas (GHG) abatement in Australia's remote, fire‐prone, and sparsely populated tropical savannas provides a financial incentive for intensive fire management that aims to reduce fire frequency, severity, and extent, and it supports important social, economic, and land management opportunities for remote communities, conservation agencies, and pastoralists. These programs now cover >20% of Australia's 1.9 million km2 tropical savanna biome, encompassing areas of globally significant biodiversity value. A common assertion is that by reducing the frequency, severity, and extent of fires for GHG abatement, these programs provide biodiversity co‐benefits. However, such biodiversity benefits have been assumed rather than demonstrated. Much better accounting of how biodiversity is responding to changed fire management is required to ensure that there are no unintended outcomes for biodiversity (bioperversity), and that biodiversity co‐benefits are maximized. Such accounting could underpin the earning of formal biodiversity credits from improved fire management, and will go a long way to understanding and improving the biodiversity outcomes of savanna fire management.
The relationship between special needs and dental trauma. A systematic review and meta‐analysis
Authors: Silveira Anna Luiza Nogueira de Miranda e Silva , Magno Marcela Baraúna , Soares Thais Rodrigues Campos 
Published: November 2019

AbstractBackground/AimsUnderstanding the risk factors for dental injuries is essential to develop prevention strategies. The aim of this study was to perform a systematic review and meta‐analysis to determine whether people with special needs (SN) have a higher incidence of traumatic dental injury (TDI).Materials and methodsElectronic searches were performed with no language or date restrictions in the following databases: PubMed, Lilacs, BBO, Scopus, Web of Science, Cochrane Library and Open Gray. According to the PECOS strategy, observational studies that investigated subjects with and without SN and its association with TDI episodes were included. Quality assessment and bias control were carried out according to Fowkes and Fulton guidelines. A meta‐analysis was performed by sub‐grouping studies according to the type of SN, with the odds ratio (OR) also being calculated (P ≤ .05). The evidence was quality tested using the GRADE approach.ResultsAfter titles and abstracts were examined, and full texts were read, 28 studies were included in the qualitative synthesis and 27 in the meta‐analysis. Three studies were classified with high methodological quality and the others had methodological problems. No associations were determined between TDI and autism spectrum disorder, epilepsy and mental disability (OR 1.12 [0.70, 1.78], OR 1.28 [0.13, 12.27] and OR 1.04 [0.20, 5.35], respectively, P > .05). A positive association (P < .05) was found between TDI and hyperactivity disorder, cerebral palsy, 21 trisomy, various conditions of SN and in pooled results (OR 2.67 [1.22, 5.87], OR 1.89 [1.06, 3.37], OR 6.18 [2.24, 17.05], OR 1.69 [1.18, 2.41], OR 1.61 [1.16; 2.22], respectively).The certainty of evidence ranged from very low to low.ConclusionsIn general, people with SN had a higher chance of having TDI with very low certainty of evidence. People with ADHD and cerebral palsy had a higher chance of TDI.