Fast level set multi-view stereo on graphics hardware

被引:0
|
作者
Labatut, Patrick [1 ]
Keriven, Renaud [1 ,2 ]
Pons, Jean-Philippe [2 ]
机构
[1] Ecole Normale Super, Dept Informat, 45 Rue Ulm, F-75005 Paris, France
[2] Ecole Natl Ponts & Chauss, CERTIS, F-77455 Paris, France
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this paper we show the importance and feasibility of much faster multi-view stereo reconstruction algorithms relying almost exclusively on graphics hardware. Reconstruction algorithms have been steadily improving in the last few years and several state-of-the-art methods are nowadays reaching a very impressive level of quality. However all these modern techniques share a very lengthy computational time that completely forbids their more widespread use in practical setups: the typical running time of such algorithms range from one to several hours. One possible solution to this problem seems to lie in the use of graphics hardware: more and more computer vision techniques are taking advantage of the availability of cheap computational horsepower and divert graphics hardware from its original purpose to accelerate the early stages of some algorithms. We present here an almost full implementation on graphics hardware of a multi-view stereo algorithm based on surface deformation by a PDE: this algorithm tries to minimize the error between input images and predicted ones by reprojection via the surface. As it mainly works on whole images, it is well suited for graphics hardware. We show how we succeeded to bring the whole reconstruction time within minutes. Results for synthetic and real data sets are presented with computational times and compared with those of other state-of-the-art algorithms.
引用
收藏
页码:774 / 781
页数:8
相关论文
共 50 条
  • [41] Shading-Aware Multi-view Stereo
    Langguth, Fabian
    Sunkavalli, Kalyan
    Hadap, Sunil
    Goesele, Michael
    COMPUTER VISION - ECCV 2016, PT III, 2016, 9907 : 469 - 485
  • [42] Multi-View Stereo on Consistent Face Topology
    Fyffe, G.
    Nagano, K.
    Huynh, L.
    Saito, S.
    Busch, J.
    Jones, A.
    Li, H.
    Debevec, P.
    COMPUTER GRAPHICS FORUM, 2017, 36 (02) : 295 - 309
  • [43] Multi-View Stereo with Learnable Cost Metric
    Yang, Guidong
    Zhou, Xunkuai
    Gao, Chuanxiang
    Zhao, Benyun
    Zhang, Jihan
    Chen, Yizhou
    Chen, Xi
    Chen, Ben M.
    2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, IROS, 2023, : 3017 - 3024
  • [44] Facetwise Mesh Refinement for Multi-View Stereo
    Romanoni, Andrea
    Matteucci, Matteo
    2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 6794 - 6801
  • [45] Attention-Aware Multi-View Stereo
    Luo, Keyang
    Guan, Tao
    Ju, Lili
    Wang, Yuesong
    Chen, Zhuo
    Luo, Yawei
    2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2020, : 1587 - 1596
  • [46] Deep Multi-View Stereo Gone Wild
    Darmon, Francois
    Bascle, Benedicte
    Devaux, Jean-Clement
    Monasse, Pascal
    Aubry, Mathieu
    2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021), 2021, : 484 - 493
  • [47] Multi-view stereo for community photo collections
    Goesele, Michael
    Snavely, Noah
    Curless, Brian
    Hoppe, Hugues
    Seitz, Steven M.
    2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, 2007, : 825 - +
  • [48] Continuous Depth Estimation for Multi-view Stereo
    Liu, Yebin
    Cao, Xun
    Dai, Qionghai
    Xu, Wenli
    CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4, 2009, : 2121 - 2128
  • [49] Multi-view Superpixel Stereo in Urban Environments
    Branislav Mičušík
    Jana Košecká
    International Journal of Computer Vision, 2010, 89 : 106 - 119
  • [50] ACCURATE MULTI-VIEW STEREO BY SELECTIVE EXPANSION
    Tian, Hu
    Li, Fei
    2017 3DTV CONFERENCE: THE TRUE VISION - CAPTURE, TRANSMISSION AND DISPLAY OF 3D VIDEO (3DTV-CON), 2017,