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
  • [1] 3D point cloud colorization by images registration
    Colorisation de nuages de points 3D par recalage dense d’images numériques
    1600, Lavoisier (31): : 1 - 2
  • [2] Colorization of point clouds 3 by resetting dense of digital images
    Crombez, Nathan
    Caron, Guillaume
    Mouaddib, El Mustapha
    TRAITEMENT DU SIGNAL, 2014, 31 (1-2) : 81 - 106
  • [3] Iterative BTreeNet: Unsupervised learning for large and dense 3D point cloud registration
    Xi, Long
    Tang, Wen
    Xue, Tao
    Wan, TaoRuan
    NEUROCOMPUTING, 2022, 506 : 336 - 354
  • [4] Learning multiview 3D point cloud registration
    Gojcic, Zan
    Zhou, Caifa
    Wegner, Jan D.
    Guibas, Leonidas J.
    Birdal, Tolga
    2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2020, : 1756 - 1766
  • [5] 3D point cloud registration algorithm with IVCCS
    Wang C.
    Li G.
    Liu X.
    Shi C.
    Qiu W.
    Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2022, 51 (06):
  • [6] 3D POINT CLOUD REGISTRATION WITH SHAPE CONSTRAINT
    Agarwal, Swapna
    Bhowmick, Brojeshwar
    2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2017, : 2199 - 2203
  • [7] Hierarchical Optimization of 3D Point Cloud Registration
    Liu, Huikai
    Zhang, Yue
    Lei, Linjian
    Xie, Hui
    Li, Yan
    Sun, Shengli
    SENSORS, 2020, 20 (23) : 1 - 20
  • [8] Improved Feature Point Algorithm for 3D Point Cloud Registration
    Kamencay, Patrik
    Sinko, Martin
    Hudec, Robert
    Benco, Miroslav
    Radil, Roman
    2019 42ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), 2019, : 517 - 520
  • [9] Point Cloud Colorization Based on Densely Annotated 3D Shape Dataset
    Cao, Xu
    Nagao, Katashi
    MULTIMEDIA MODELING (MMM 2019), PT I, 2019, 11295 : 436 - 446
  • [10] Extraction of soybean coverage from UAV images combined with 3D dense point cloud
    He H.
    Yan Y.
    Ling M.
    Yang Q.
    Chen T.
    Li L.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2022, 38 (02): : 201 - 209