2017 IEEE International Conference on Computer Vision (ICCV)最新文献_第5页

AOD-Net: All-in-One Dehazing Network AOD-Net:一体化除雾网络

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.511

Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng

引用次数: 1096

Generative Modeling of Audible Shapes for Object Perception 面向对象感知的可听形状生成建模

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.141

Zhoutong Zhang, Jiajun Wu, Qiujia Li, Zhengjia Huang, James Traer, Josh H. McDermott, J. Tenenbaum, W. Freeman

引用次数: 35

BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera BodyFusion:使用单个深度相机实时捕获人体运动和表面几何形状

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.104

Tao Yu, Kaiwen Guo, F. Xu, Yuan Dong, Zhaoqi Su, Jianhui Zhao, Jianguo Li, Qionghai Dai, Yebin Liu

{"title":"BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera","authors":"Tao Yu, Kaiwen Guo, F. Xu, Yuan Dong, Zhaoqi Su, Jianhui Zhao, Jianguo Li, Qionghai Dai, Yebin Liu","doi":"10.1109/ICCV.2017.104","DOIUrl":"https://doi.org/10.1109/ICCV.2017.104","url":null,"abstract":"We propose BodyFusion, a novel real-time geometry fusion method that can track and reconstruct non-rigid surface motion of a human performance using a single consumer-grade depth camera. To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method. The key feature of our method is that it jointly solves for both the skeleton and graph-node deformations based on information of the attachments between the skeleton and the graph nodes. The attachments are also updated frame by frame based on the fused surface geometry and the computed deformations. Overall, our method enables increasingly denoised, detailed, and complete surface reconstruction as well as the updating of the skeleton and attachments as the temporal depth frames are fused. Experimental results show that our method exhibits substantially improved nonrigid motion fusion performance and tracking robustness compared with previous state-of-the-art fusion methods. We also contribute a dataset for the quantitative evaluation of fusion-based dynamic scene reconstruction algorithms using a single depth camera.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"6 1","pages":"910-919"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74216470","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: 160

A Coarse-Fine Network for Keypoint Localization 关键点定位的粗-精网络

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.329

Shaoli Huang, Mingming Gong, D. Tao

{"title":"A Coarse-Fine Network for Keypoint Localization","authors":"Shaoli Huang, Mingming Gong, D. Tao","doi":"10.1109/ICCV.2017.329","DOIUrl":"https://doi.org/10.1109/ICCV.2017.329","url":null,"abstract":"We propose a coarse-fine network (CFN) that exploits multi-level supervisions for keypoint localization. Recently, convolutional neural networks (CNNs)-based methods have achieved great success due to the powerful hierarchical features in CNNs. These methods typically use confidence maps generated from ground-truth keypoint locations as supervisory signals. However, while some keypoints can be easily located with high accuracy, many of them are hard to localize due to appearance ambiguity. Thus, using strict supervision often fails to detect keypoints that are difficult to locate accurately To target this problem, we develop a keypoint localization network composed of several coarse detector branches, each of which is built on top of a feature layer in a CNN, and a fine detector branch built on top of multiple feature layers. We supervise each branch by a specified label map to explicate a certain supervision strictness level. All the branches are unified principally to produce the final accurate keypoint locations. We demonstrate the efficacy, efficiency, and generality of our method on several benchmarks for multiple tasks including bird part localization and human body pose estimation. Especially, our method achieves 72.2% AP on the 2016 COCO Keypoints Challenge dataset, which is an 18% improvement over the winning entry.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"106 1","pages":"3047-3056"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79257455","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: 146

A Joint Intrinsic-Extrinsic Prior Model for Retinex 视网膜的联合内在-外在先验模型

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.431

Bolun Cai, Xian-shun Xu, K. Guo, K. Jia, B. Hu, D. Tao

引用次数: 173

Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification 车辆再识别的方向不变特征嵌入与时空正则化

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.49

Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang

{"title":"Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification","authors":"Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang","doi":"10.1109/ICCV.2017.49","DOIUrl":"https://doi.org/10.1109/ICCV.2017.49","url":null,"abstract":"In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"29 1","pages":"379-387"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82241690","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: 314

Rolling-Shutter-Aware Differential SfM and Image Rectification 滚动快门感知差分SfM和图像校正

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.108

Bingbing Zhuang, L. Cheong, Gim Hee Lee

{"title":"Rolling-Shutter-Aware Differential SfM and Image Rectification","authors":"Bingbing Zhuang, L. Cheong, Gim Hee Lee","doi":"10.1109/ICCV.2017.108","DOIUrl":"https://doi.org/10.1109/ICCV.2017.108","url":null,"abstract":"In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two consecutive frames despite of Rolling Shutter (RS) artifacts. In particular, we show that under constant velocity assumption, the errors induced by the rolling shutter effect can be easily rectified by a linear scaling operation on each optical flow. We further propose a 9-point algorithm to recover the relative pose of a rolling shutter camera that undergoes constant acceleration motion. We demonstrate that the dense depth maps recovered from the relative pose of the RS camera can be used in a RS-aware warping for image rectification to recover high-quality Global Shutter (GS) images. Experiments on both synthetic and real RS images show that our RS-aware differential SfM algorithm produces more accurate results on relative pose estimation and 3D reconstruction from images distorted by RS effect compared to standard SfM algorithms that assume a GS camera model. We also demonstrate that our RS-aware warping for image rectification method outperforms state-of-the-art commercial software products, i.e. Adobe After Effects and Apple Imovie, at removing RS artifacts.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"46 1","pages":"948-956"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80996521","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: 57

Estimating Defocus Blur via Rank of Local Patches 通过局部补丁的秩估计散焦模糊

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.574

Guodong Xu, Yuhui Quan, Hui Ji

引用次数: 38

Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-Rank and Invariant Sparse Decomposition 基于低秩和不变稀疏分解的延时或运动触发图像序列中的运动目标检测

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.548

M. Shakeri, Hong Zhang

引用次数: 20

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes 用于街景语义理解的Mapillary远景数据集

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.534

Gerhard Neuhold, Tobias Ollmann, S. R. Bulò, P. Kontschieder

引用次数: 984