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Light Field Salient Object Detection: A Review And Benchmark, Keren Fu, Yao Jiang, Ge-Peng Ji, Tao Zhou, Qijun Zhao, Deng-Ping Fan 2022 College of Computer Science, Sichuan University, and National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China

Light Field Salient Object Detection: A Review And Benchmark, Keren Fu, Yao Jiang, Ge-Peng Ji, Tao Zhou, Qijun Zhao, Deng-Ping Fan

Computational Visual Media

Salient object detection (SOD) is a long-standing research topic in computer vision with increasing interest in the past decade. Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways, using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend. This paper provides the first comprehensive review and a benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep ...


Recent Advances In Glinty Appearance Rendering, Junqiu Zhu, Sizhe Zhao, Yanning Xu, Xiangxu Meng, Lu Wang, Ling-Qi Yan 2022 Shandong University, Jinan, China

Recent Advances In Glinty Appearance Rendering, Junqiu Zhu, Sizhe Zhao, Yanning Xu, Xiangxu Meng, Lu Wang, Ling-Qi Yan

Computational Visual Media

The interaction between light and materials is key to physically-based realistic rendering. However, it is also complex to analyze, especially when the materials contain a large number of details and thus exhibit "glinty" visual effects. Recent methods of producing glinty appearance are expected to be important in next-generation computer graphics. We provide here a comprehensive survey on recent glinty appearance rendering. We start with a definition of glinty appearance based on microfacet theory, and then summarize research works in terms of representation and practical rendering. We have implemented typical methods using our unified platform and compare them in terms of ...


Automatic Location And Semantic Labeling Of Landmarks On 3d Human Body Models, Shan Luo, Qitong Zhang, Jieqing Feng 2022 State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China

Automatic Location And Semantic Labeling Of Landmarks On 3d Human Body Models, Shan Luo, Qitong Zhang, Jieqing Feng

Computational Visual Media

Landmarks on human body models are of great significance for applications such as digital anthropometry and clothing design. The diversity of pose and shape of human body models and the semantic gap make landmarking a challenging problem. Inthis paper, a learning-based method is proposed to locate landmarks on human body models by analyzing the relationship between geometric descriptors and semantic labels of landmarks. A shape alignmentalgorithm is proposed to align human body models to break symmetric ambiguity. A symmetry-awaredescriptor is proposed based on the structure of the human body models, which is robust to both pose and shape variations in ...


Constructing Self-Supporting Surfaces With Planar Quadrilateral Elements, Long Ma, Sidan Yao, Jianmin Zheng, Yang Liu, Yuanfeng Zhou, Shi-Qing Xin, Ying He 2022 School of Software, Shandong University, Jinan 250101, China;School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore

Constructing Self-Supporting Surfaces With Planar Quadrilateral Elements, Long Ma, Sidan Yao, Jianmin Zheng, Yang Liu, Yuanfeng Zhou, Shi-Qing Xin, Ying He

Computational Visual Media

We present a simple yet effective method for constructing 3D self-supporting surfaces with planar quadrilateral (PQ) elements. Starting with a triangular discretization of a self-supporting surface, we firstcompute the principal curvatures and directions of each triangular face using a new discrete differential geometryapproach, yielding more accurate results than existing methods. Then, we smooth the principal direction field to reduce the number of singularities. Next, we partition all faces into two groups in terms of principalcurvature difference. For each face with small curvature difference, we compute a stretch matrix that turns the principal directions into a pair of conjugate directions. For ...


Blnet: Bidirectional Learning Network For Point Clouds, Wenkai Han, Hai Wu, Chenglu Wen, Cheng Wang, Xin Li 2022 School of Informatics, Xiamen University, 422 Siming South Road, Xiamen 361005, China

Blnet: Bidirectional Learning Network For Point Clouds, Wenkai Han, Hai Wu, Chenglu Wen, Cheng Wang, Xin Li

Computational Visual Media

The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points. By relying on per-point multi-layer perceptions (MLPs), most existing point-based approaches only address the first issue yet ignore the second one. Directly convolving kernels with irregular points will result in loss of shape information. This paper introduces a novel point-based bidirectional learning network (BLNet) to analyze irregular 3D points. BLNet optimizes the learning of 3D points through two iterative operations: feature-guided point shifting and feature learning from shifted points, so as to minimise intra-class variances, leading to a more regular ...


Joint Self-Supervised And Reference-Guided Learning For Depth Inpainting, Heng Wu, Kui Fu, Yifan Zhao, Haokun Song, Jia Li 2022 State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beihang University, Beijing 100191, China

Joint Self-Supervised And Reference-Guided Learning For Depth Inpainting, Heng Wu, Kui Fu, Yifan Zhao, Haokun Song, Jia Li

Computational Visual Media

Depth information can benefit various computer vision tasks on both images and videos. However, depth maps may suffer from invalid values in many pixels, and also large holes. To improve such data, we propose a joint self-supervised and reference-guided learning approach for depth inpainting. For the self-supervised learning strategy, we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information. This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map. Then, both the learned convolutional dictionary and sparse coding are convolved ...


Image-Guided Color Mapping For Categorical Data Visualization, Qian Zheng, Min Lu, Sicong Wu, Ruizhen Hu, Joel Lanir, Hui Huang 2022 Suzhou University of Science and Technology, Suzhou 215009, China

Image-Guided Color Mapping For Categorical Data Visualization, Qian Zheng, Min Lu, Sicong Wu, Ruizhen Hu, Joel Lanir, Hui Huang

Computational Visual Media

Appropriate color mapping for categorical data visualization can significantly facilitate the discovery of underlying data patterns and effectively bring out visual aesthetics. Some systems suggest pre-defined palettes for this task. However, a predefined color mapping is not always optimal, failing to consider users’ needs for customization. Given an input cate-gorical data visualization and a reference image, we present an effective method to automatically generate a coloring that resembles the reference while allowing classes to be easily distinguished. We extract a color palette with high perceptual distance between the colors by sampling dominant and discriminable colors from the image’s color ...


Self-Supervised Coarse-To-Fine Monocular Depth Estimation Using A Lightweight Attention Module, Yuanzhen Li, Fei Luo, Chunxia Xiao 2022 School of Computer Science, Wuhan University, Wuhan 430072, China

Self-Supervised Coarse-To-Fine Monocular Depth Estimation Using A Lightweight Attention Module, Yuanzhen Li, Fei Luo, Chunxia Xiao

Computational Visual Media

Self-supervised monocular depth estimation has been widely investigated and applied in previous works. However, existing methods suffer from texture-copy, depth drift, and incomplete structure. It is difficult for normal CNN networks to completely understand the relationship between the object and its surrounding environment. Moreover, it is hard to design the depth smoothness loss to balance depth smoothness and sharpness. To address these issues, we propose a coarse-to-fine method with a normalized convolutional block attention module (NCBAM). In the coarse estimation stage, we incorporate the NCBAM into depth and pose networks to overcome the texture-copy and depth drift problems. Then, we ...


High Fidelity Virtual Try-On Network Via Semantic Adaptation And Distributed Componentization, Chenghu Du, Feng Yu, Minghua Jiang, Ailing Hua, Yaxin Zhao, Xiong Wei, Tao Peng, Xinrong Hu 2022 School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China

High Fidelity Virtual Try-On Network Via Semantic Adaptation And Distributed Componentization, Chenghu Du, Feng Yu, Minghua Jiang, Ailing Hua, Yaxin Zhao, Xiong Wei, Tao Peng, Xinrong Hu

Computational Visual Media

Image-based virtual try-on systems have significant commercial value in online garment shopping. However, prior methods fail to appropriately handle details, so are defective in maintaining the original appearance of organizational items including arms, the neck, and in-shop garments. We propose a novel high fidelity virtual try-on network to generate realistic results. Specifically, a distributed pipeline is used for simultaneous generation of organizational items. First, the in-shop garment is warped using thin plate splines (TPS) to give a coarse shape reference, and then a corresponding target semantic map is generated, which can adaptively respond to the distribution of different items triggered ...


Using Strategic Options Development And Analysis (Soda) To Understand The Simulation Accessibility Problem, Andrew J. Collins, Ying Thaviphoke, Antuela A. Tako 2022 Old Dominion University

Using Strategic Options Development And Analysis (Soda) To Understand The Simulation Accessibility Problem, Andrew J. Collins, Ying Thaviphoke, Antuela A. Tako

Engineering Management & Systems Engineering Faculty Publications

Simulation modelling is applied to a wide range of problems, including defense and healthcare. However, there is a concern within the simulation community that there is a limited use and implementation of simulation studies in practice. This suggests that despite its benefits, simulation may not be reaching its potential in making a real-world impact. The main reason for this could be that simulation tools are not widely accessible in industry. In this paper, we investigate the issues that affect simulation modelling accessibility through a workshop with simulation practitioners. We use Strategic Options Development and Analysis (SODA), a problem-structuring approach that ...


Pvt V2: Improved Baselines With Pyramid Vision Transformer, Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao 2022 Shanghai AI Laboratory, Shanghai 200232, China;Department of Computer Science and Technology, NanjingUniversity, Nanjing 210023, China

Pvt V2: Improved Baselines With Pyramid Vision Transformer, Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao

Computational Visual Media

Transformers have recently lead to encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding three designs: (i) a linear complexity attention layer, (ii) an overlapping patch embedding, and (iii) a convolutional feed-forward network. With these modifications, PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification, detection, and segmentation. In particular, PVT v2 achieves comparable or better performance than recent work such as the Swin transformer. We hope this work will facilitate state-of-the-art transformer ...


Attention Mechanisms In Computer Vision: A Survey, Meng-Hao Guo, Tian-Xing Xu, Jiang-Jiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai-Jiang Mu, Song-Hai Zhang, Ralph R. Martin, Ming-Ming Cheng, Shi-Min Hu 2022 BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Attention Mechanisms In Computer Vision: A Survey, Meng-Hao Guo, Tian-Xing Xu, Jiang-Jiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai-Jiang Mu, Song-Hai Zhang, Ralph R. Martin, Ming-Ming Cheng, Shi-Min Hu

Computational Visual Media

Humans can naturally and effectively find salient regions in complex scenes. Motivated by thisobservation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according ...


High-Quality Indoor Scene 3d Reconstruction With Rgb-D Cameras: A Brief Review, Jianwei Li, Wei Gao, Yihong Wu, Yangdong Liu, Yanfei Shen 2022 School of Sports Engineering, Beijing Sports University, Beijing 100084, China

High-Quality Indoor Scene 3d Reconstruction With Rgb-D Cameras: A Brief Review, Jianwei Li, Wei Gao, Yihong Wu, Yangdong Liu, Yanfei Shen

Computational Visual Media

High-quality 3D reconstruction is an important topic in computer graphics and computer vision with many applications, such as robotics and augmented reality. The advent of consumer RGB-D cameras has made a profound advance in indoor scenereconstruction. For the past few years, researchers have spent significant effort to develop algorithms to capture 3D models with RGB-D cameras. As depth images produced by consumer RGB-D cameras are noisy and incomplete when surfaces are shiny, bright, transparent, or far from the camera, obtaining high- quality 3D scene models is still a challenge for existing systems. We here review high-quality 3D indoor scene reconstruction ...


Robust And Efficient Edge-Based Visual Odometry, Feihu Yan, Zhaoxin Li, Zhong Zhou 2022 State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China

Robust And Efficient Edge-Based Visual Odometry, Feihu Yan, Zhaoxin Li, Zhong Zhou

Computational Visual Media

Visual odometry, which aims to estimate relative camera motion between sequential video frames, has been widely used in the fields of augmented reality, virtual reality, and autonomous driving. However, it is still quite challenging for state-of-the-art approaches to handle low-texture scenes. In this paper, we propose a robust and efficient visual odometry algorithm that directly utilizes edge pixels to track camera pose. In contrast to direct methods, we choose reprojection error to construct the optimization energy, which can effectively cope with illumination changes. The distance transform map built upon edge detection for each frame is used to improve tracking efficiency ...


Message From The Best Paper Award Committee, Ming C. Lin, Xin Tong, Wenping Wang 2022 University of Maryland at College Park, USA

Message From The Best Paper Award Committee, Ming C. Lin, Xin Tong, Wenping Wang

Computational Visual Media

No abstract provided.


Rendering Discrete Participating Media Using Geometrical Optics Approximation, Jie Guo, Bingyang Hu, Yanjun Chen, Yuanqi Li, Yanwen Guo, Ling-Qi Yan 2022 State Key Lab for Novel Software Technology, Nanjing University, Nanjing 210023, China

Rendering Discrete Participating Media Using Geometrical Optics Approximation, Jie Guo, Bingyang Hu, Yanjun Chen, Yuanqi Li, Yanwen Guo, Ling-Qi Yan

Computational Visual Media

We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles. The particle size is expected to range from the scale of the wavelength to several orders of magnitude greater, resulting in an appearance with distinct graininess as opposed to the smooth appearance of continuous media. One fundamental issue in the physically-based synthesis of such appearance is to determine the necessary optical properties in every local region. Since these properties vary spatially, we resort to geometrical optics approximation (GOA), a highly efficient alternative to rigorous Lorenz-Mie theory, to quantitatively represent the scattering of a ...


Progressive Edge-Sensing Dynamic Scene Deblurring, Tianlin Zhang, Jinjiang Li, Hui Fan 2022 School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China;Institute of ZhongKe Network Technology, Yantai 264005, China

Progressive Edge-Sensing Dynamic Scene Deblurring, Tianlin Zhang, Jinjiang Li, Hui Fan

Computational Visual Media

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors. In recent years, the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied. We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams. Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges. The coarse-to-fine architecture restores the image structure, first performing global adjustments, and ...


Arm3d: Attention-Based Relation Module For Indoor 3d Object Detection, Yuqing Lan, Yao Duan, Chenyi Liu, Chenyang Zhu, Yueshan Xiong, Hui Huang, Kai Xu 2022 College of Computer, National University of Defense Technology, Changsha 410073, China

Arm3d: Attention-Based Relation Module For Indoor 3d Object Detection, Yuqing Lan, Yao Duan, Chenyi Liu, Chenyang Zhu, Yueshan Xiong, Hui Huang, Kai Xu

Computational Visual Media

Relation contexts have been proved to be useful for many challenging vision tasks. In the field of 3D object detection, previous methods have been taking the advantage of context encoding, graph embedding, orexplicit relation reasoning to extract relation contexts. However, there exist inevitably redundant relation contexts due to noisy or low-quality proposals. In fact, invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity, which may, on the contrary, reduce the performance in complex scenes. Inspired by recent attention mechanism like Transformer, we propose a novel 3D attention-based relation module (ARM3D). It encompasses object-aware relation reasoning to extract pair-wise relation ...


Nprportrait 1.0: A Three-Level Benchmark For Non-Photorealistic Rendering Of Portraits, Paul L. Rosin, Yu-Kun Lai, David Mould, Ran Yi, Itamar Berger, Lars Doyle, Seungyong Lee, Chuan Li, Yong-Jin Liu, Amir Semmo, Ariel Shamir, Minjung Son, Holger Winnemöller 2022 School of Computer Science and Informatics, Cardiff University, Cardiff, UK

Nprportrait 1.0: A Three-Level Benchmark For Non-Photorealistic Rendering Of Portraits, Paul L. Rosin, Yu-Kun Lai, David Mould, Ran Yi, Itamar Berger, Lars Doyle, Seungyong Lee, Chuan Li, Yong-Jin Liu, Amir Semmo, Ariel Shamir, Minjung Son, Holger Winnemöller

Computational Visual Media

Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, thetask of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its ...


Aogan: A Generative Adversarial Network For Screen Space Ambient Occlusion, Lei Ren, Ying Song 2022 Zhejiang Sci-Tech University, Hangzhou 310018, China

Aogan: A Generative Adversarial Network For Screen Space Ambient Occlusion, Lei Ren, Ying Song

Computational Visual Media

Ambient occlusion (AO) is a widely-used real-time rendering technique which estimates light intensity on visible scene surfaces. Recently, a number of learning-based AO approaches have been proposed, which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and speed. However, most such methods have high error for complex scenes or tend to ignore details. We propose an end-to-end generative adversarial network for the production of realistic AO, and explore the importance of perceptual loss in the generative model to AO accuracy. An attention mechanism is also described to improve the accuracy of ...


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