2016 Fourth International Conference on 3D Vision (3DV)最新文献_第8页

Synthesizing Training Images for Boosting Human 3D Pose Estimation 合成训练图像增强人体三维姿态估计

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-04-10 DOI: 10.1109/3DV.2016.58

Wenzheng Chen, Huan Wang, Yangyan Li, Hao Su, Zhenhua Wang, Changhe Tu, D. Lischinski, D. Cohen-Or, Baoquan Chen

{"title":"Synthesizing Training Images for Boosting Human 3D Pose Estimation","authors":"Wenzheng Chen, Huan Wang, Yangyan Li, Hao Su, Zhenhua Wang, Changhe Tu, D. Lischinski, D. Cohen-Or, Baoquan Chen","doi":"10.1109/3DV.2016.58","DOIUrl":"https://doi.org/10.1109/3DV.2016.58","url":null,"abstract":"Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126948504","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: 269

Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images 辐射场景分解:RGB-D图像的场景反射率、照明和几何形状

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-04-05 DOI: 10.1109/3DV.2016.39

Stephen Lombardi, K. Nishino

{"title":"Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images","authors":"Stephen Lombardi, K. Nishino","doi":"10.1109/3DV.2016.39","DOIUrl":"https://doi.org/10.1109/3DV.2016.39","url":null,"abstract":"Recovering the radiometric properties of a scene (i.e., the reflectance, illumination, and geometry) is a long-sought ability of computer vision that can provide invaluable information for a wide range of applications. Deciphering the radiometric ingredients from the appearance of a real-world scene, as opposed to a single isolated object, is particularly challenging as it generally consists of various objects with different material compositions exhibiting complex reflectance and light interactions that are also part of the illumination. We introduce the first method for radiometric decomposition of real-world scenes that handles those intricacies. We use RGB-D images to bootstrap geometry recovery and simultaneously recover the complex reflectance and natural illumination while refining the noisy initial geometry and segmenting the scene into different material regions. Most important, we handle real-world scenes consisting of multiple objects of unknown materials, which necessitates the modeling of spatially-varying complex reflectance, natural illumination, texture, interreflection and shadows. We systematically evaluate the effectiveness of our method on synthetic scenes and demonstrate its application to real-world scenes. The results show that rich radiometric information can be recovered from RGB-D images and demonstrate a new role RGB-D sensors can play for general scene understanding tasks.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124973452","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: 38

HDRFusion: HDR SLAM Using a Low-Cost Auto-Exposure RGB-D Sensor HDRFusion:使用低成本自动曝光RGB-D传感器的HDR SLAM

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-04-04 DOI: 10.1109/3DV.2016.40

Shuda Li, Ankur Handa, Yang Zhang, A. Calway

引用次数: 20

Learning to Navigate the Energy Landscape 学会驾驭能源格局

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-03-18 DOI: 10.1109/3DV.2016.41

Julien P. C. Valentin, Angela Dai, M. Nießner, Pushmeet Kohli, Philip H. S. Torr, S. Izadi, Cem Keskin

引用次数: 127

3D Saliency for Finding Landmark Buildings 寻找地标建筑的3D显着性

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 1900-01-01 DOI: 10.1109/3DV.2016.35

Nikolay Kobyshev, Hayko Riemenschneider, A. Bódis-Szomorú, L. Gool

引用次数: 6