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G2mf-Wa: Geometric Multi-Model Fitting With Weakly Annotated Data, Chao Zhang, Xuequan Lu, Katsuya Hotta, Xi Yang 2020 University of Fukui, Fukui, 910-8507, Japan.

G2mf-Wa: Geometric Multi-Model Fitting With Weakly Annotated Data, Chao Zhang, Xuequan Lu, Katsuya Hotta, Xi Yang

Computational Visual Media

In this paper we address the problem ofgeometric multi-model fitting using a few weakly annotated data points, which has been little studied so far. In weak annotating (WA), most manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. Such WA data can naturally arise through interaction in various tasks. For example, in the case of homography estimation, one can easily annotate points on the same plane or object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of WA data to boost multi-model fitting performance ...


Learning Local Shape Descriptors For Computing Non-Rigid Dense Correspondence, Jianwei Guo, Hanyu Wang, Zhanglin Cheng, Xiaopeng Zhang, Dong-Ming Yan 2020 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Learning Local Shape Descriptors For Computing Non-Rigid Dense Correspondence, Jianwei Guo, Hanyu Wang, Zhanglin Cheng, Xiaopeng Zhang, Dong-Ming Yan

Computational Visual Media

A discriminative local shape descriptor plays an important role in various applications. In this paper, we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes. We use local "geometry images" to encode the multi-scale local features of a point, via an intrinsic parameterization method based on geodesic polar coordinates. This new parameterization provides robust geometry images even for badly-shaped triangular meshes. Then a triplet network with shared architecture and parameters is used to perform deep metric learning; its aim is to distinguish between similar and dissimilar pairs of points. Additionally, a newly designed triplet ...


Vr Content Creation And Exploration With Deep Learning: A Survey, Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang 2020 State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China. Peng Cheng Laboratory, Shenzhen 518000, China.

Vr Content Creation And Exploration With Deep Learning: A Survey, Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang

Computational Visual Media

Virtual reality (VR) offers an artificial, com-puter generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large quantities of visual media data. Intelligence of VR methods and applications has been significantly boosted by the recent developmentsin deep learning techniques. VR content creationand exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully ...


A Practical Path Guiding Method For Participating Media, Hong Deng, Beibei Wang, Rui Wang, Nicolas Holzschuch 2020 Nanjing University of Science and Technology, Nanjing 210094, China.

A Practical Path Guiding Method For Participating Media, Hong Deng, Beibei Wang, Rui Wang, Nicolas Holzschuch

Computational Visual Media

Rendering translucent materials is costly: light transport algorithms need to simulate a large number of scattering events inside the material before reaching convergence. The cost is especially high for materials with a large albedo or a small mean-free-path, where higher-order scattering effects dominate. In simple terms, the paths get lost in the medium. Path guiding has been proposed for surface rendering to make convergence faster by guiding the sampling process. In this paper, we introduce a path guiding solution for translucent materials. We learn an adaptive approximate representation of the radiance distribution in the volume and use it to sample ...


What And Where: A Context-Based Recommendation System For Object Insertion, Song-Hai Zhang, Zheng-Ping Zhou, Bin Liu, Xi Dong, Peter Hall 2020 Tsinghua University, Beijing 100084, China. Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.

What And Where: A Context-Based Recommendation System For Object Insertion, Song-Hai Zhang, Zheng-Ping Zhou, Bin Liu, Xi Dong, Peter Hall

Computational Visual Media

We propose a novel problem revolving around two tasks: (i) given a scene, recommend objects to insert, and (ii) given an object category, retrieve suitable background scenes. A bounding box for the inserted object is predicted in both tasks, which helps downstream applications such as semi-automated advertising and video composition. The major challenge lies in the fact that the target object is neither present nor localized in the input, and furthermore, available datasets only provide scenes with existing objects. To tackle this problem, we build an unsupervised algorithm based on object-level contexts, which explicitly models the joint probability distribution of ...


Waternet: An Adaptive Matching Pipeline For Segmenting Water With Volatile Appearance, Yongqing Liang, Navid Jafari, Xing Luo, Qin Chen, Yanpeng Cao, Xin Li 2020 School of Electrical Engineering and Computer Science, Louisiana State University, USA.

Waternet: An Adaptive Matching Pipeline For Segmenting Water With Volatile Appearance, Yongqing Liang, Navid Jafari, Xing Luo, Qin Chen, Yanpeng Cao, Xin Li

Computational Visual Media

We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide seg-mentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects such as water, whose appearance is non-uniform and changing dynamically, our pipeline can produce more reliable and accurate segmentation results than existing algorithms.


Efficient Ray Casting Of Volumetric Images Using Distance Maps For Empty Space Skipping, Lachlan J. Deakin, Mark A. Knackstedt 2020 Australian National University, Canberra, 2601, Australia.

Efficient Ray Casting Of Volumetric Images Using Distance Maps For Empty Space Skipping, Lachlan J. Deakin, Mark A. Knackstedt

Computational Visual Media

Volume and isosurface rendering are methods of projecting volumetric images to two dimensions for visualisation. These methods are common in medical imaging and scientific visualisation.Head-mounted optical see-through displays have recently become an affordable technology and are a promising platform for volumetric image visualisation. Images displayed on a head-mounted display must be presented at a high frame rate and with low latency to compensate for head motion. High latency can be jarring and may cause cybersickness which has similar symptoms to motion sickness.Volumetric images can be very computationally expensive to render as they often have hundreds of millions of ...


Real-Time Rendering Of Layered Materials With Anisotropic Normal Distributions, Tomoya Yamaguchi, Tatsuya Yatagawa, Yusuke Tokuyoshi, Shigeo Morishima 2020 Waseda University, Tokyo, 155-8885, Japan.

Real-Time Rendering Of Layered Materials With Anisotropic Normal Distributions, Tomoya Yamaguchi, Tatsuya Yatagawa, Yusuke Tokuyoshi, Shigeo Morishima

Computational Visual Media

This paper proposes a lightweight bi-directional scattering distribution function (BSDF) model for layered materials with anisotropic reflection and refraction properties. In our method, each layer of the materials can be described by a microfacet BSDF using an anisotropic normal distribution function (NDF). Furthermore, the NDFs of layers can be defined on tangent vector fields, which differ from layer to layer. Our method is based on a previous study in which isotropic BSDFs are approximated by projecting them onto base planes. However, the adequateness of this previous work has not been well investigated for anisotropic BSDFs. In this paper, we demonstrate ...


Reconstructing Piecewise Planar Scenes With Multi-View Regularization, Weijie Xi, Xuejin Chen 2020 University of Science and Technology of China, Hefei, 230026, China.

Reconstructing Piecewise Planar Scenes With Multi-View Regularization, Weijie Xi, Xuejin Chen

Computational Visual Media

Reconstruction of man-made scenes from multi-view images is an important problem in computer vision and computer graphics. Observing that man-made scenes are usually composed of planar surfaces, we encode plane shape prior in reconstructing man-made scenes. Recent approaches for single-view reconstruction employ multi-branch neural networks to simultaneouslysegment planes and recover 3D plane parameters. However, the scale of available annotated data heavily limits the generalizability and accuracy of these supervised methods. In this paper, we propose multi-view regularization to enhance the capability of piecewise planar reconstruction during the training phase, without demanding extra annotated data. Our multi-view regularization enables the consistency ...


Adaptive Deep Residual Network For Single Image Super-Resolution, Shuai Liu, Ruipeng Gang, Chenghua Li, Ruixia Song 2020 North China University of Technology, Beijing, 100043, China.

Adaptive Deep Residual Network For Single Image Super-Resolution, Shuai Liu, Ruipeng Gang, Chenghua Li, Ruixia Song

Computational Visual Media

In recent years, deep learning has achieved great success in the field of image processing. In the single image super-resolution (SISR) task, the con-volutional neural network (CNN) extracts the features of the image through deeper layers, and has achieved impressive results. In this paper, we propose a singleimage super-resolution model based on Adaptive Deep Residual named as ADR-SR, which uses the Input Output Same Size (IOSS) structure, and releases the dependence of upsampling layers compared with the existing SR methods. Specifically, the key element of our model is the Adaptive Residual Block (ARB), which replaces the commonly used constant factor ...


Insocialnet: Interactive Visual Analytics For Role-Event Videos, Yaohua Pan, Zhibin Niu, Jing Wu, Jiawan Zhang 2020 College of Intelligence and Computing, and School of New Media and Communication, Tianjin University, Tianjin, 300354, China.

Insocialnet: Interactive Visual Analytics For Role-Event Videos, Yaohua Pan, Zhibin Niu, Jing Wu, Jiawan Zhang

Computational Visual Media

Role-event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysisof the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role-event videos. It automatically and dynamically constructs social networks from role-event videos making use of face and expression recognition, and provides a visual interface for interactive ...


Mixed Reality Based Respiratory Liver Tumor Puncture Navigation, Ruotong Li, Weixin Si, Xiangyun Liao, Qiong Wang, Reinhard Klein, Pheng-Ann Heng 2020 Institute of Computer Science II, University of Bonn, 53115 Bonn, Germany.

Mixed Reality Based Respiratory Liver Tumor Puncture Navigation, Ruotong Li, Weixin Si, Xiangyun Liao, Qiong Wang, Reinhard Klein, Pheng-Ann Heng

Computational Visual Media

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Oursystem contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene. Compared with traditional navigation method with CT, our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we ...


Spinnet: Spinning Convolutional Network For Lane Boundary Detection, Ruochen Fan, Xuanrun Wang, Qibin Hou, Hanchao Liu, Tai-Jiang Mu 2020 Tsinghua University, Beijing, 100084, China.

Spinnet: Spinning Convolutional Network For Lane Boundary Detection, Ruochen Fan, Xuanrun Wang, Qibin Hou, Hanchao Liu, Tai-Jiang Mu

Computational Visual Media

In this paper, we propose a simple but effective framework for lane boundary detection, called SpinNet. Considering that cars or pedestrians often occlude lane boundaries and that the local features of lane boundaries are not distinctive, therefore, analyzing and collecting global context information is crucial for lane boundary detection. To this end, we design a novel spinning convolution layer and a brand-new lane parameterization branch in our network to detect lane boundaries from a global perspective. To extract features in narrow strip-shaped fields, we adopt strip-shaped convolutions with kernels which have 1×n or n×1 shape in the spinning ...


A Three-Stage Real-Time Detector For Traffic Signs In Large Panoramas, Yizhi Song, Ruochen Fan, Sharon Huang, Zhe Zhu, Ruofeng Tong 2020 Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN 47907, USA.

A Three-Stage Real-Time Detector For Traffic Signs In Large Panoramas, Yizhi Song, Ruochen Fan, Sharon Huang, Zhe Zhu, Ruofeng Tong

Computational Visual Media

Traffic sign detection is one of the key com-ponents in autonomous driving. Advanced autonomous vehicles armed with high quality sensors capture high definition images for further analysis. Detecting traffic signs, moving vehicles, and lanes is important for localization and decision making. Traffic signs, especially those that are far from the camera, are small, and so are challenging to traditional object detection methods. In this work, in order to reduce computational cost and improve detection performance, we split the large input images into small blocks and then recognize traffic signs in the blocks using another detection module. Therefore, this paper proposes ...


Evaluation Of Modified Adaptive K-Means Segmentation Algorithm, Taye Girma Debelee, Friedhelm Schwenker, Samuel Rahimeto, Dereje Yohannes 2020 Institute of Neural Information Processing, Ulm University, 89081Ulm, Germany; Addis Ababa Science and Technology University, Addis Ababa, 120611, Ethiopia.

Evaluation Of Modified Adaptive K-Means Segmentation Algorithm, Taye Girma Debelee, Friedhelm Schwenker, Samuel Rahimeto, Dereje Yohannes

Computational Visual Media

Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI).The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The ...


Practical Brdf Reconstruction Using Reliable Geometric Regions From Multi-View Stereo, Taishi Ono, Hiroyuki Kubo, Kenichiro Tanaka, Takuya Funatomi, Yasuhiro Mukaigawa 2020 Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.

Practical Brdf Reconstruction Using Reliable Geometric Regions From Multi-View Stereo, Taishi Ono, Hiroyuki Kubo, Kenichiro Tanaka, Takuya Funatomi, Yasuhiro Mukaigawa

Computational Visual Media

In this paper, we present a practical methodfor reconstructing the bidirectional reflectance distribu-tion function (BRDF) from multiple images of a real object composed of a homogeneous material. The key idea is that the BRDF can be sampled after geometry estimation using multi-view stereo (MVS) techniques. Our contribution is selection of reliable samples of lighting, surface normal, and viewing directions for robustness against estimation errors of MVS. Our method is quantitatively evaluated using synthesized images and its effectiveness is shown via real-world experiments.


Single Image Shadow Removal By Optimization Using Non-Shadow Anchor Values, Saritha Murali, V. K. Govindan, Saidalavi Kalady 2020 Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala 673601, India.

Single Image Shadow Removal By Optimization Using Non-Shadow Anchor Values, Saritha Murali, V. K. Govindan, Saidalavi Kalady

Computational Visual Media

Shadow removal has evolved as a pre-processing step for various computer vision tasks. Several studies have been carried out over the past two decades to eliminate shadows from videos and images. Accurate shadow detection is an open problem because it is often considered difficult to interpret whether the darkness of a surface is contributed by a shadow incident on it or not. This paper introduces a color-model based technique to remove shadows from images. We formulate shadow removal as an optimization problem that minimizes the dissimilarities between a shadow area and its non-shadow counterpart. To achieve this, we map each ...


Unsupervised Natural Image Patch Learning, Dov Danon, Hadar Averbuch-Elor, Ohad Fried, Daniel Cohen-Or 2020 Tel-Aviv University, Tel Aviv 6997801, Israel.

Unsupervised Natural Image Patch Learning, Dov Danon, Hadar Averbuch-Elor, Ohad Fried, Daniel Cohen-Or

Computational Visual Media

A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner, requiring the availability of many annotated images. In this paper, we present an unsupervised embedding of natural image patches, avoiding the need for annotated images. The key idea is that the similarity of two patches can be learned from the prevalence of their spatial proximity in natural images ...


Automatic Route Planning For Gps Art Generation, Andre Waschk, Jens Krüger 2020 University of Duisburg-Essen, 47057 Duisburg, Germany.

Automatic Route Planning For Gps Art Generation, Andre Waschk, Jens Krüger

Computational Visual Media

In this paper, we present a novel approach to automated route generation of global positioning system (GPS) artwork. The term GPS artwork describes the generation of drawings by leaving virtual traces on digital maps. Until now, the creation of these images has required a manual planning phase in which an artist designs the route by hand. Once the route for this artwork has been planned, GPS devices have been used to track the movement. Using our solution, the lengthy planning phase can be significantly shortened, thereby opening art creation to a broader public.


Object Removal From Complex Videos Using A Few Annotations, Thuc Trinh Le, Andrés Almansa, Yann Gousseau, Simon Masnou 2020 LTCI, Télécom ParisTech, Université Paris-Saclay, 75013 Paris, France.

Object Removal From Complex Videos Using A Few Annotations, Thuc Trinh Le, Andrés Almansa, Yann Gousseau, Simon Masnou

Computational Visual Media

We present a system for the removal of objects from videos. As input, the system only needs a user to draw a few strokes on the first frame, roughly delimiting the objects to be removed. To the best of our knowledge, this is the first system allowing the semi-automatic removal of objects from videos with complex backgrounds. The key steps of our system are the following: after initialization, segmentation masks are first refined and then automatically propagated through the video. Missing regions are then synthesized using video inpainting techniques. Our system can deal with multiple, possibly crossing objects, with complex ...


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