3D POINT CLOUD MODEL COLORIZATION BY DENSE REGISTRATION OF DIGITAL IMAGES

被引:13
|
作者
Crombez, N. [1 ]
Caron, G. [1 ]
Mouaddib, E. [1 ]
机构
[1] Univ Picardie, MIS Lab, F-80039 Amiens 1, France
关键词
Point clouds; Virtual and Visual Servoing; Images Registration; Colorization;
D O I
10.5194/isprsarchives-XL-5-W4-123-2015
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Architectural heritage is a historic and artistic property which has to be protected, preserved, restored and must be shown to the public. Modern tools like 3D laser scanners are more and more used in heritage documentation. Most of the time, the 3D laser scanner is completed by a digital camera which is used to enrich the accurate geometric informations with the scanned objects colors. However, the photometric quality of the acquired point clouds is generally rather low because of several problems presented below. We propose an accurate method for registering digital images acquired from any viewpoints on point clouds which is a crucial step for a good colorization by colors projection. We express this image-to-geometry registration as a pose estimation problem. The camera pose is computed using the entire images intensities under a photometric visual and virtual servoing (VVS) framework. The camera extrinsic and intrinsic parameters are automatically estimated. Because we estimates the intrinsic parameters we do not need any informations about the camera which took the used digital image. Finally, when the point cloud model and the digital image are correctly registered, we project the 3D model in the digital image frame and assign new colors to the visible points. The performance of the approach is proven in simulation and real experiments on indoor and outdoor datasets of the cathedral of Amiens, which highlight the success of our method, leading to point clouds with better photometric quality and resolution.
引用
收藏
页码:123 / 130
页数:8
相关论文
共 50 条
  • [21] 3D Body Point Cloud Data Denoising and Registration
    Li, Xiaozhi
    Li, Xiaojiu
    ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL II, PROCEEDINGS, 2009, : 587 - 590
  • [22] Rigid 3D Point Cloud Registration Based on Point Feature Histograms
    Wang, Xi
    Zhang, Xutang
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MACHINERY, ELECTRONICS AND CONTROL SIMULATION (MECS 2017), 2017, 138 : 543 - 550
  • [23] A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration
    Marani, Roberto
    Reno, Vito
    Nitti, Massimiliano
    D'Orazio, Tiziana
    Stella, Ettore
    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2016, 31 (07) : 515 - 534
  • [24] An Automatic Dense Point Registration Method for 3D Face Animation
    Hu, Yongli
    Zhou, Mingquan
    Wu, Zhongke
    PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9, 2009, : 2325 - 2330
  • [25] Hybrid3D: learning 3D hybrid features with point clouds and multi-view images for point cloud registration
    Bangbang YANG
    Zhaoyang HUANG
    Yijin LI
    Han ZHOU
    Hongsheng LI
    Guofeng ZHANG
    Hujun BAO
    Science China(Information Sciences), 2023, 66 (07) : 77 - 93
  • [26] Hybrid3D: learning 3D hybrid features with point clouds and multi-view images for point cloud registration
    Yang, Bangbang
    Huang, Zhaoyang
    Li, Yijin
    Zhou, Han
    Li, Hongsheng
    Zhang, Guofeng
    Bao, Hujun
    SCIENCE CHINA-INFORMATION SCIENCES, 2023, 66 (07)
  • [27] An effective approach for 3D point cloud registration in railway contexts
    Patruno, Cosimo
    Colella, Roberto
    Nitti, Massimiliano
    Stella, Ettore
    MULTIMODAL SENSING: TECHNOLOGIES AND APPLICATIONS, 2019, 11059
  • [28] Optimization of ICP point cloud registration in plants 3D modeling
    Lu J.
    Lan Y.
    Wu Z.
    Liang X.
    Chang H.
    Deng X.
    Wu Z.
    Tang Y.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2022, 38 (02): : 183 - 191
  • [29] A 3D Point Cloud Registration Algorithm based on Feature Points
    Ren, Yi
    Zhou, Fucai
    PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INFORMATION SCIENCES, MACHINERY, MATERIALS AND ENERGY (ICISMME 2015), 2015, 126 : 803 - 807
  • [30] CorsNet: 3D Point Cloud Registration by Deep Neural Network
    Kurobe, Akiyoshi
    Sekikawa, Yusuke
    Ishikawa, Kohta
    Saito, Hideo
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 5 (03): : 3960 - 3966