Texture Illumination Separation for Single-Shot Structured Light Reconstruction

被引:19
|
作者
Vo, Minh [1 ]
Narasimhan, Srinivasa G. [1 ]
Sheikh, Yaser [1 ]
机构
[1] Carnegie Mellon Univ, Inst Robot, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会;
关键词
Single-shot; decomposition; separation; illumination; texture; mixture; IMAGES; TRANSPARENT; PROJECTION;
D O I
10.1109/TPAMI.2015.2443775
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Active illumination based methods have a trade-off between acquisition time and resolution of the estimated 3D shapes. Multi-shot approaches can generate dense reconstructions but require stationary scenes. Single-shot methods are applicable to dynamic objects but can only estimate sparse reconstructions and are sensitive to surface texture. We present a single-shot approach to produce dense shape reconstructions of highly textured objects illuminated by one or more projectors. The key to our approach is an image decomposition scheme that can recover the illumination image of different projectors and the texture images of the scene from their mixed appearances. We focus on three cases of mixed appearances: the illumination from one projector onto textured surface, illumination from multiple projectors onto a textureless surface, or their combined effect. Our method can accurately compute per-pixel warps from the illumination patterns and the texture template to the observed image. The texture template is obtained by interleaving the projection sequence with an all-white pattern. The estimated warps are reliable even with infrequent interleaved projection and strong object deformation. Thus, we obtain detailed shape reconstruction and dense motion tracking of the textured surfaces. The proposed method, implemented using a one camera and two projectors system, is validated on synthetic and real data containing subtle non-rigid surface deformations.
引用
收藏
页码:390 / 404
页数:15
相关论文
共 50 条
  • [1] Separating Texture and Illumination for Single-Shot Structured Light Reconstruction
    Vo, Minh
    Narasimhan, Srinivasa G.
    Sheikh, Yaser
    2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2014, : 433 - 440
  • [2] Single-shot photofragment imaging by structured illumination
    Larsson, Kajsa
    Jonsson, Malin
    Borggren, Jesper
    Kristensson, Elias
    Ehn, Andreas
    Alden, Marcus
    Bood, Joakim
    OPTICS LETTERS, 2015, 40 (21) : 5019 - 5022
  • [3] The-Dimensional Hand Reconstruction by Single-Shot Structured Light Line Pattern
    Wang, Zhenzhou
    Zhang, Cunshan
    IEEE ACCESS, 2018, 6 : 59881 - 59890
  • [4] Single-shot videography with multiplex structured illumination using an interferometer
    Shibata, Tomoaki
    Omachi, Junko
    OPTICS EXPRESS, 2023, 31 (16) : 27020 - 27028
  • [5] Single-shot optical sectioning microscopy based on structured illumination
    Fu, Zhiqiang
    Chen, Jialong
    Liu, Gan
    Chen, Shih-Chi
    OPTICS LETTERS, 2022, 47 (04) : 814 - 817
  • [6] Single-shot autofocusing in light sheet fluorescence microscopy with multiplexed structured illumination and deep learning
    Gan, Yanhong
    Ye, Zitong
    Han, Yubing
    Ma, Ye
    Li, Chuankang
    Liu, Qiulan
    Liu, Wenjie
    Kuang, Cuifang
    Liu, Xu
    OPTICS AND LASERS IN ENGINEERING, 2023, 168
  • [7] Self-Distilled Depth From Single-Shot Structured Light With Intensity Reconstruction
    Li, Yue
    Peng, Jiayong
    Zhang, Yueyi
    Xiong, Zhiwei
    IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2023, 9 : 678 - 691
  • [8] Single-shot three-dimensional reconstruction based on structured light line pattern
    Wang, ZhenZhou
    Yang, YongMing
    OPTICS AND LASERS IN ENGINEERING, 2018, 106 : 10 - 16
  • [9] 3D Reconstruction with Single-Shot Structured Light RGB Line Pattern
    Li, Yikang
    Wang, Zhenzhou
    SENSORS, 2021, 21 (14)
  • [10] Single-Shot Structured Light Sensor for 3D Dense and Dynamic Reconstruction
    Gu, Feifei
    Song, Zhan
    Zhao, Zilong
    SENSORS, 2020, 20 (04)