2016 Fourth International Conference on 3D Vision (3DV)最新文献_第6页

Single-Shot Time-of-Flight Phase Unwrapping Using Two Modulation Frequencies 使用两个调制频率的单次飞行时间相位展开

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.74

Changpeng Ti, Ruigang Yang, James Davis

引用次数: 2

A Depth Restoration Occlusionless Temporal Dataset 一个深度恢复无遮挡时间数据集

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.26

Daniel Rotman, Guy Gilboa

{"title":"A Depth Restoration Occlusionless Temporal Dataset","authors":"Daniel Rotman, Guy Gilboa","doi":"10.1109/3DV.2016.26","DOIUrl":"https://doi.org/10.1109/3DV.2016.26","url":null,"abstract":"Depth restoration, the task of correcting depth noise and artifacts, has recently risen in popularity due to the increase in commodity depth cameras. When assessing the quality of existing methods, most researchers resort to the popular Middlebury dataset, however, this dataset was not created for depth enhancement, and therefore lacks the option of comparing genuine low-quality depth images with their high-quality, ground-truth counterparts. To address this shortcoming, we present the Depth Restoration Occlusionless Temporal (DROT) dataset. This dataset offers real depth sensor input coupled with registered pixel-to-pixel color images, and the ground-truth depth to which we wish to compare. Our dataset includes not only Kinect 1 and Kinect 2 data, but also an Intel R200 sensor intended for integration into hand-held devices. Beyond this, we present a new temporal depth-restoration method. Utilizing multiple frames, we create a number of possibilities for an initial degraded depth map, which allows us to arrive at a more educated decision when refining depth images. Evaluating this method with our dataset shows significant benefits, particularly for overcoming real sensor-noise artifacts.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"14 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":"122063429","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: 7

Quaternionic Upsampling: Hyperspherical Techniques for 6 DoF Pose Tracking 四元数上采样:超球面技术的6自由度姿态跟踪

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.71

Benjamin Busam, M. Esposito, B. Frisch, Nassir Navab

{"title":"Quaternionic Upsampling: Hyperspherical Techniques for 6 DoF Pose Tracking","authors":"Benjamin Busam, M. Esposito, B. Frisch, Nassir Navab","doi":"10.1109/3DV.2016.71","DOIUrl":"https://doi.org/10.1109/3DV.2016.71","url":null,"abstract":"Fast real-time tracking is an integral component of modern 3D computer vision pipelines. Despite their advantages in accuracy and reliability, optical trackers suffer from limited acquisition rates depending either on intrinsic sensor capabilities or physical limitations such as exposure time. Moreover, data transmission and image processing produce latency in the pose stream. We introduce quaternionic upsampling to overcome these problems. The technique models the pose parameters as points on multidimensional hyperspheres in (dual) quaternion space. In order to upsample the pose stream, we present several methods to sample points on geodesics and piecewise continuous curves on these manifolds and compare them regarding accuracy and computation efficiency. With the unified approach of quaternionic upsampling, both interpolation and extrapolation in pose space can be done by continuous linear variation of only one sampling parameter. Since the method can be implemented rather efficiently, pose rates of over 4 kHz and future pose predictions with an accuracy of 128 μm and 0.5° are possible in real-time. The method does not depend on a special tracking algorithm and can thus be used for any arbitrary 3 DoF or 6 DoF rotation or pose tracking system.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"15 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":"122205990","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

Robust Plane-Based Calibration of Multiple Non-Overlapping Cameras 基于鲁棒平面的多相机非重叠标定

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.73

Chen Zhu, Zihan Zhou, Ziran Xing, Yanbing Dong, Yi Ma, Jingyi Yu

引用次数: 13

Synthetic Prior Design for Real-Time Face Tracking 实时人脸跟踪的综合先验设计

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.72

Steven G. McDonagh, M. Klaudiny, D. Bradley, T. Beeler, Iain Matthews, Kenny Mitchell

{"title":"Synthetic Prior Design for Real-Time Face Tracking","authors":"Steven G. McDonagh, M. Klaudiny, D. Bradley, T. Beeler, Iain Matthews, Kenny Mitchell","doi":"10.1109/3DV.2016.72","DOIUrl":"https://doi.org/10.1109/3DV.2016.72","url":null,"abstract":"Real-time facial performance capture has recently been gaining popularity in virtual film production, driven by advances in machine learning, which allows for fast inference of facial geometry from video streams. These learning-based approaches are significantly influenced by the quality and amount of labelled training data. Tedious construction of training sets from real imagery can be replaced by rendering a facial animation rig under on-set conditions expected at runtime. We learn a synthetic actor-specific prior by adapting a state-of-the-art facial tracking method. Synthetic training significantly reduces the capture and annotation burden and in theory allows generation of an arbitrary amount of data. But practical realities such as training time and compute resources still limit the size of any training set. We construct better and smaller training sets by investigating which facial image appearances are crucial for tracking accuracy, covering the dimensions of expression, viewpoint and illumination. A reduction of training data in 1-2 orders of magnitude is demonstrated whilst tracking accuracy is retained for challenging on-set footage.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"80 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":"126399742","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: 16

Robust Tracking in Low Light and Sudden Illumination Changes 弱光和突然光照变化下的鲁棒跟踪

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.48

Hatem Alismail, Brett Browning, S. Lucey

引用次数: 29

Model-Based Outdoor Performance Capture 基于模型的户外表现捕捉

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.25

Nadia Robertini, D. Casas, Helge Rhodin, H. Seidel, C. Theobalt

引用次数: 43

Multiview RGB-D Dataset for Object Instance Detection 用于对象实例检测的多视图RGB-D数据集

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-09-26 DOI: 10.1109/3DV.2016.52

G. Georgakis, Md. Alimoor Reza, Arsalan Mousavian, P. Le, J. Kosecka

引用次数: 75

Fast Single Shot Detection and Pose Estimation 快速单镜头检测和姿态估计

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-09-19 DOI: 10.1109/3DV.2016.78

Patrick Poirson, Phil Ammirato, Cheng-Yang Fu, Wei Liu, J. Kosecka, A. Berg

{"title":"Fast Single Shot Detection and Pose Estimation","authors":"Patrick Poirson, Phil Ammirato, Cheng-Yang Fu, Wei Liu, J. Kosecka, A. Berg","doi":"10.1109/3DV.2016.78","DOIUrl":"https://doi.org/10.1109/3DV.2016.78","url":null,"abstract":"For applications in navigation and robotics, estimating the 3D pose of objects is as important as detection. Many approaches to pose estimation rely on detecting or tracking parts or keypoints [11, 21]. In this paper we build on a recent state-of-the-art convolutional network for sliding-window detection [10] to provide detection and rough pose estimation in a single shot, without intermediate stages of detecting parts or initial bounding boxes. While not the first system to treat pose estimation as a categorization problem, this is the first attempt to combine detection and pose estimation at the same level using a deep learning approach. The key to the architecture is a deep convolutional network where scores for the presence of an object category, the offset for its location, and the approximate pose are all estimated on a regular grid of locations in the image. The resulting system is as accurate as recent work on pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly faster (46 frames per second (FPS) on a TITAN X GPU). This approach to detection and rough pose estimation is fast and accurate enough to be widely applied as a pre-processing step for tasks including high-accuracy pose estimation, object tracking and localization, and vSLAM.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132554540","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: 107

Consistent Discretization and Minimization of the L1 Norm on Manifolds 流形上L1范数的一致离散化与最小化

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-09-18 DOI: 10.1109/3DV.2016.53

A. Bronstein, Yoni Choukroun, R. Kimmel, Matan Sela

引用次数: 14