2016 Fourth International Conference on 3D Vision (3DV)最新文献_第5页

Rapid Hand Shape Reconstruction with Chebyshev Phase Shifting 切比雪夫相移快速手形重建

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.24

Daniel Moreno, Wook-Yeon Hwang, G. Taubin

引用次数: 4

A Large-Scale 3D Object Recognition Dataset 一个大规模的三维物体识别数据集

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.16

Thomas Sølund, A. Buch, N. Krüger, H. Aanæs

{"title":"A Large-Scale 3D Object Recognition Dataset","authors":"Thomas Sølund, A. Buch, N. Krüger, H. Aanæs","doi":"10.1109/3DV.2016.16","DOIUrl":"https://doi.org/10.1109/3DV.2016.16","url":null,"abstract":"This paper presents a new large scale dataset targeting evaluation of local shape descriptors and 3d object recognition algorithms. The dataset consists of point clouds and triangulated meshes from 292 physical scenes taken from 11 different views, a total of approximately 3204 views. Each of the physical scenes contain 10 occluded objects resulting in a dataset with 32040 unique object poses and 45 different object models. The 45 object models are full 360 degree models which are scanned with a high precision structured light scanner and a turntable. All the included objects belong to different geometric groups, concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat and cylindrical objects. It is our objective that this dataset contributes to the future development of next generation of 3D object recognition algorithms. The dataset is public available at http://roboimagedata.compute.dtu.dk/.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"32 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":"121413401","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: 10

Exemplar-Based 3D Shape Segmentation in Point Clouds 基于范例的点云三维形状分割

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.29

Rongqi Qiu, U. Neumann

引用次数: 6

Single View 3D Reconstruction under an Uncalibrated Camera and an Unknown Mirror Sphere 未标定相机和未知镜球下的单视图三维重建

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.50

K. Han, Kwan-Yee Kenneth Wong, Xiao Tan

{"title":"Single View 3D Reconstruction under an Uncalibrated Camera and an Unknown Mirror Sphere","authors":"K. Han, Kwan-Yee Kenneth Wong, Xiao Tan","doi":"10.1109/3DV.2016.50","DOIUrl":"https://doi.org/10.1109/3DV.2016.50","url":null,"abstract":"In this paper, we develop a novel self-calibration method for single view 3D reconstruction using a mirror sphere. Unlike other mirror sphere based reconstruction methods, our method needs neither the intrinsic parameters of the camera, nor the position and radius of the sphere be known. Based on eigen decomposition of the matrix representing the conic image of the sphere and enforcing a repeated eignvalue constraint, we derive an analytical solution for recovering the focal length of the camera given its principal point. We then introduce a robust algorithm for estimating both the principal point and the focal length of the camera by minimizing the differences between focal lengths estimated from multiple images of the sphere. We also present a novel approach for estimating both the principal point and focal length of the camera in the case of just one single image of the sphere. With the estimated camera intrinsic parameters, the position(s) of the sphere can be readily retrieved from the eigen decomposition(s) and a scaled 3D reconstruction follows. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our approach.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"3 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":"127057287","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: 3

Discriminative Filters for Depth from Defocus 离焦深度的判别滤波器

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.67

Fahim Mannan, M. Langer

引用次数: 0

Multi-Label Semantic 3D Reconstruction Using Voxel Blocks 使用体素块的多标签语义三维重建

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.68

Ian Cherabier, Christian Häne, Martin R. Oswald, M. Pollefeys

{"title":"Multi-Label Semantic 3D Reconstruction Using Voxel Blocks","authors":"Ian Cherabier, Christian Häne, Martin R. Oswald, M. Pollefeys","doi":"10.1109/3DV.2016.68","DOIUrl":"https://doi.org/10.1109/3DV.2016.68","url":null,"abstract":"Techniques that jointly perform dense 3D reconstruction and semantic segmentation have recently shown very promising results. One major restriction so far is that they can often only handle a very low number of semantic labels. This is mostly due to their high memory consumption caused by the necessity to store indicator variables for every label and transition. We propose a way to reduce the memory consumption of existing methods. Our approach is based on the observation that many semantic labels are only present at very localized positions in the scene, such as cars. Therefore this label does not need to be active at every location. We exploit this observation by dividing the scene into blocks in which generally only a subset of labels is active. By determining early on in the reconstruction process which labels need to be active in which block the memory consumption can be significantly reduced. In order to recover from mistakes we propose to update the set of active labels during the iterative optimization procedure based on the current solution. We also propose a way to initialize the set of active labels using a boosted classifier. In our experimental evaluation we show the reduction of memory usage quantitatively. Eventually, we show results of joint semantic 3D reconstruction and semantic segmentation with significantly more labels than previous approaches were able to handle.","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":"129324696","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: 45

Camera Motion from Group Synchronization 相机运动从组同步

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.64

F. Arrigoni, Andrea Fusiello, B. Rossi

引用次数: 16

Depth from Gradients in Dense Light Fields for Object Reconstruction 用于物体重建的密集光场梯度深度

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.33

Kaan Yücer, Changil Kim, A. Sorkine-Hornung, O. Sorkine-Hornung

{"title":"Depth from Gradients in Dense Light Fields for Object Reconstruction","authors":"Kaan Yücer, Changil Kim, A. Sorkine-Hornung, O. Sorkine-Hornung","doi":"10.1109/3DV.2016.33","DOIUrl":"https://doi.org/10.1109/3DV.2016.33","url":null,"abstract":"Objects with thin features and fine details are challenging for most multi-view stereo techniques, since such features occupy small volumes and are usually only visible in a small portion of the available views. In this paper, we present an efficient algorithm to reconstruct intricate objects using densely sampled light fields. At the heart of our technique lies a novel approach to compute per-pixel depth values by exploiting local gradient information in densely sampled light fields. This approach can generate accurate depth values for very thin features, and can be run for each pixel in parallel. We assess the reliability of our depth estimates using a novel two-sided photoconsistency measure, which can capture whether the pixel lies on a texture or a silhouette edge. This information is then used to propagate the depth estimates at high gradient regions to smooth parts of the views efficiently and reliably using edge-aware filtering. In the last step, the per-image depth values and color information are aggregated in 3D space using a voting scheme, allowing the reconstruction of a globally consistent mesh for the object. Our approach can process large video datasets very efficiently and at the same time generates high quality object reconstructions that compare favorably to the results of state-of-the-art multi-view stereo methods.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"297 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":"125151147","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: 21

Robust Recovery of Heavily Degraded Depth Measurements 严重退化深度测量的稳健恢复

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.15

Gilad Drozdov, Yevgengy Shapiro, Guy Gilboa

引用次数: 10

Detecting and Correcting Shadows in Urban Point Clouds and Image Collections 城市点云和图像集中的阴影检测与校正

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.63

M. Guislain, Julie Digne, R. Chaine, Dimitri Kudelski, Pascal Lefebvre-Albaret, Lefebvre-Albaret. Detecting

{"title":"Detecting and Correcting Shadows in Urban Point Clouds and Image Collections","authors":"M. Guislain, Julie Digne, R. Chaine, Dimitri Kudelski, Pascal Lefebvre-Albaret, Lefebvre-Albaret. Detecting","doi":"10.1109/3DV.2016.63","DOIUrl":"https://doi.org/10.1109/3DV.2016.63","url":null,"abstract":"LiDAR (Light Detection And Ranging) acquisition is a widespread method for measuring urban scenes, be it a small town neighborhood or an entire city. It is even more interesting when this acquisition is coupled with a collection of pictures registered with the data, permitting to recover the color information of the points. Yet, this added color can be perturbed by shadows that are very dependent on the sun direction and weather conditions during the acquisition. In this paper, we focus on the problem of automatically detecting and correcting the shadows from the LiDAR data by exploiting both the images and the point set laser reflectance. Building on the observation that shadow boundaries are characterized by both a significant color change and a stable laser reflectance, we propose to first detect shadow boundaries in the point set and then segment ground shadows using graph cuts in the image. Finally using a simplified illumination model we correct the shadows directly on the colored point sets. This joint exploitation of both the laser point set and the images renders our approach robust and efficient, avoiding user interaction.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"26 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922864","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