2016 Fourth International Conference on 3D Vision (3DV)最新文献_第4页

Progressive 3D Modeling All the Way 渐进式3D建模所有的方式

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.11

Alex Locher, M. Havlena, L. Gool

引用次数: 2

Deep Stereo Fusion: Combining Multiple Disparity Hypotheses with Deep-Learning 深度立体融合:多视差假设与深度学习的结合

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.22

Matteo Poggi, S. Mattoccia

{"title":"Deep Stereo Fusion: Combining Multiple Disparity Hypotheses with Deep-Learning","authors":"Matteo Poggi, S. Mattoccia","doi":"10.1109/3DV.2016.22","DOIUrl":"https://doi.org/10.1109/3DV.2016.22","url":null,"abstract":"Stereo matching is a popular technique to infer depth from two or more images and wealth of methods have been proposed to deal with this problem. Despite these efforts, finding accurate stereo correspondences is still an open problem. The strengths and weaknesses of existing methods are often complementary and in this paper, motivated by recent trends in this field, we exploit this fact by proposing Deep Stereo Fusion, a Convolutional Neural Network capable of combining the output of multiple stereo algorithms in order to obtain more accurate result with respect to each input disparity map. Deep Stereo Fusion process a 3D features vector, encoding both spatial and cross-algorithm information, in order to select the best disparity hypothesis among those proposed by the single stereo matchers. To the best of our knowledge, our proposal is the first i) to leverage on deep learning and ii) able to predict the optimal disparity assignments by taking only as input cue the disparity maps. This second feature makes our method suitable for deployment even when other cues (e.g., confidence) are not available such as when dealing with disparity maps provided by off-the-shelf 3D sensors. We thoroughly evaluate our proposal on the KITTI stereo benchmark with respect state-of-the-art in this field.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122213297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Video Depth-from-Defocus 视频Depth-from-Defocus

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.46

Hyeongwoo Kim, Christian Richardt, C. Theobalt

{"title":"Video Depth-from-Defocus","authors":"Hyeongwoo Kim, Christian Richardt, C. Theobalt","doi":"10.1109/3DV.2016.46","DOIUrl":"https://doi.org/10.1109/3DV.2016.46","url":null,"abstract":"Many compelling video post-processing effects, in particular aesthetic focus editing and refocusing effects, are feasible if per-frame depth information is available. Existing computational methods to capture RGB and depth either purposefully modify the optics (coded aperture, light-field imaging), or employ active RGB-D cameras. Since these methods are less practical for users with normal cameras, we present an algorithm to capture all-in-focus RGB-D video of dynamic scenes with an unmodified commodity video camera. Our algorithm turns the often unwanted defocus blur into a valuable signal. The input to our method is a video in which the focus plane is continuously moving back and forth during capture, and thus defocus blur is provoked and strongly visible. This can be achieved by manually turning the focus ring of the lens during recording. The core algorithmic ingredient is a new video-based depth-from-defocus algorithm that computes space-time-coherent depth maps, deblurred all-in-focus video, and the focus distance for each frame. We extensively evaluate our approach, and show that it enables compelling video post-processing effects, such as different types of refocusing.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130078926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Tracking Deformable Surfaces That Undergo Topological Changes Using an RGB-D Camera 使用RGB-D相机跟踪经过拓扑变化的可变形表面

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.42

Aggeliki Tsoli, Antonis A. Argyros

{"title":"Tracking Deformable Surfaces That Undergo Topological Changes Using an RGB-D Camera","authors":"Aggeliki Tsoli, Antonis A. Argyros","doi":"10.1109/3DV.2016.42","DOIUrl":"https://doi.org/10.1109/3DV.2016.42","url":null,"abstract":"We present a method for 3D tracking of deformable surfaces with dynamic topology, for instance a paper that undergoes cutting or tearing. Existing template-based methods assume a template of fixed topology. Thus, they fail in tracking deformable objects that undergo topological changes. In our work, we employ a dynamic template (3D mesh) whose topology evolves based on the topological changes of the observed geometry. Our tracking framework deforms the defined template based on three types of constraints: (a) the surface of the template has to be registered to the 3D shape of the tracked surface, (b) the template deformation should respect feature (SIFT) correspondences between selected pairs of frames, and (c) the lengths of the template edges should be preserved. The latter constraint is relaxed when an edge is found to lie on a \"geometric gap\", that is, when a significant depth discontinuity is detected along this edge. The topology of the template is updated on the fly by removing overstretched edges that lie on a geometric gap. The proposed method has been evaluated quantitatively and qualitatively in both synthetic and real sequences of monocular RGB-D views of surfaces that undergo various types of topological changes. The obtained results show that our approach tracks effectively objects with evolving topology and outperforms state of the art methods in tracking accuracy.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129798439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Coupled Functional Maps 耦合功能映射

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.49

D. Eynard, E. Rodolà, K. Glashoff, M. Bronstein

引用次数: 51

A Closed-Form Bayesian Fusion Equation Using Occupancy Probabilities 基于占用概率的封闭贝叶斯融合方程

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.47

Charles T. Loop, Q. Cai, Sergio Orts, P. Chou

引用次数: 22

3D Human Pose Estimation via Deep Learning from 2D Annotations 基于2D注释的深度学习三维人体姿态估计

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.84

Ernesto Brau, Hao Jiang

引用次数: 47

Will It Last? Learning Stable Features for Long-Term Visual Localization 这种情况会持续下去吗?学习长期视觉定位的稳定特征

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.3929/ETHZ-A-010819535

Marcin Dymczyk, E. Stumm, Juan I. Nieto, R. Siegwart, Igor Gilitschenski

{"title":"Will It Last? Learning Stable Features for Long-Term Visual Localization","authors":"Marcin Dymczyk, E. Stumm, Juan I. Nieto, R. Siegwart, Igor Gilitschenski","doi":"10.3929/ETHZ-A-010819535","DOIUrl":"https://doi.org/10.3929/ETHZ-A-010819535","url":null,"abstract":"An increasing number of simultaneous localization and mapping (SLAM) systems are using appearance-based localization to improve the quality of pose estimates. However, with the growing time-spans and size of the areas we want to cover, appearance-based maps are often becoming too large to handle and are consisting of features that are not always reliable for localization purposes. This paper presents a method for selecting map features that are persistent over time and thus suited for long-term localization. Our methodology relies on a CNN classifier based on image patches and depth maps for recognizing which features are suitable for life-long matchability. Thus, the classifier not only considers the appearance of a feature but also takes into account its expected lifetime. As a result, our feature selection approach produces more compact maps with a high fraction of temporally-stable features compared to the current state-of-the-art, while rejecting unstable features that typically harm localization. Our approach is validated on indoor and outdoor datasets, that span over a period of several months.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

SceneNN: A Scene Meshes Dataset with aNNotations SceneNN:一个带有注释的场景网格数据集

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.18

Binh-Son Hua, Quang-Hieu Pham, D. Nguyen, Minh-Khoi Tran, L. Yu, Sai-Kit Yeung

引用次数: 272

X-Tag: A Fiducial Tag for Flexible and Accurate Bundle Adjustment X-Tag:灵活准确的束调整基准标签

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.65

Tolga Birdal, Ievgeniia Dobryden, Slobodan Ilic

{"title":"X-Tag: A Fiducial Tag for Flexible and Accurate Bundle Adjustment","authors":"Tolga Birdal, Ievgeniia Dobryden, Slobodan Ilic","doi":"10.1109/3DV.2016.65","DOIUrl":"https://doi.org/10.1109/3DV.2016.65","url":null,"abstract":"In this paper we design a novel planar 2D fiducial marker and develop fast detection algorithm aiming easy camera calibration and precise 3D reconstruction at the marker locations via the bundle adjustment. Even though an abundance of planar fiducial markers have been made and used in various tasks, none of them has properties necessary to solve the aforementioned tasks. Our marker, X-tag, enjoys a novel design, coupled with very efficient and robust detection scheme, resulting in a reduced number of false positives. This is achieved by constructing markers with random circular features in the image domain and encoding them using two true perspective invariants: cross-ratios and intersection preservation constraints. To detect the markers, we developed an effective search scheme, similar to Geometric Hashing and Hough Voting, in which the marker decoding is cast as a retrieval problem. We apply our system to the task of camera calibration and bundle adjustment. With qualitative and quantitative experiments, we demonstrate the robustness and accuracy of X-tag in spite of blur, noise, perspective and radial distortions, and showcase camera calibration, bundle adjustment and 3d fusion of depth data from precise extrinsic camera poses.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123982928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18