NavOScan: hassle-free handheld 3D scanning with automatic multi-view registration based on combined optical and inertial pose estimation

被引:1
|
作者
Munkelt, C. [1 ]
Kleiner, B. [2 ]
Thorhallsson, T. [3 ]
Mendoza, C. [3 ]
Braeuer-Burchardt, C. [1 ]
Kuehmstedt, P. [1 ]
Notni, G. [1 ]
机构
[1] Fraunhofer Inst Appl Opt & Precis Engn IOF, Jena, Germany
[2] Fraunhofer Inst Mfg Engn & Automat IPA, Jena, Germany
[3] Innovat Ctr Iceland ICI, Reykjavik, Iceland
关键词
optical 3D reconstruction; handheld sensor; inertial measurement unit;
D O I
10.1117/12.2020759
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Portable 3D scanners with low measurement uncertainty are ideally suited for capturing the 3D shape of objects right in their natural environment. However, elaborate manual post processing was usually necessary to build a complete 3D model from several overlapping scans (multiple views), or expensive or complex additional hardware (like trackers etc.) was needed. On the contrary, the NavOScan project[1] aims at fully automatic multi-view 3D scan assembly through a Navigation Unit attached to the scanner. This light weight device combines an optical tracking system with an inertial measurement unit (IMU) for robust relative scanner position estimation. The IMU provides robustness against swift scanner movements during view changes, while the wide angle, high dynamic range (HDR) optical tracker focused on the measurement object and its background ensures accurate sensor position estimations. The underlying software framework, partly implemented in hardware (FPGA) for performance reasons, fusions both data streams in real time and estimates the navigation unit's current pose. Using this pose to calculate the starting solution of the Iterative Closest Point registration approach allows for automatic registration of multiple 3D scans. After finishing the individual scans required to fully acquire the object in question, the operator is readily presented with its finalized complete 3D model! The paper presents an overview over the NavOScan architecture, highlights key aspects of the registration and navigation pipeline and shows several measurement examples obtained with the Navigation Unit attached to a hand held structured-light 3D scanner.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Handheld 3-D Scanning with Automatic Multi-View Registration Based on Visual-Inertial Navigation
    Kleiner, Bernhard
    Munkelt, Christoph
    Thorhallsson, Torfi
    Notni, Gunther
    Kuehmstedt, Peter
    Schneider, Urs
    INTERNATIONAL JOURNAL OF OPTOMECHATRONICS, 2014, 8 (04) : 313 - 325
  • [2] Generative Multi-View Based 3D Human Pose Estimation
    Sabri, Motaz
    PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON SUSTAINABLE INFORMATION ENGINEERING AND TECHNOLOGY, SIET 2021, 2021, : 2 - 9
  • [3] 3D Registration of Multi-view Depth Data for Hand-Arm Pose Estimation
    Ha, Yeongmin
    Shin, Seho
    Park, Jaeheung
    2014 11TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI), 2014, : 653 - 657
  • [4] Automated 3D scan multi-view registration based on rotation estimation
    Wang, Huaxin
    Vergeest, Joris S. M.
    Song, Yu
    Wiegers, Tjamme
    WSCG 2007, FULL PAPERS PROCEEDINGS I AND II, 2007, : 137 - 144
  • [5] RF-based Multi-view Pose Machine for Multi-Person 3D Pose Estimation
    Xie, Chunyang
    Zhang, Dongheng
    Wu, Zhi
    Yu, Cong
    Hu, Yang
    Sun, Qibin
    Chen, Yan
    2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME, 2023, : 2669 - 2674
  • [6] Multi-view Pictorial Structures for 3D Human Pose Estimation
    Amin, Sikandar
    Andriluka, Mykhaylo
    Rohrbach, Marcus
    Schiele, Bernt
    PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2013, 2013,
  • [7] Direct Multi-view Multi-person 3D Pose Estimation
    Wang, Tao
    Zhang, Jianfeng
    Cai, Yujun
    Yan, Shuicheng
    Feng, Jiashi
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [8] Multi-view 3D Human Pose Estimation in Complex Environment
    M. Hofmann
    D. M. Gavrila
    International Journal of Computer Vision, 2012, 96 : 103 - 124
  • [9] PROGRESSIVE MULTI-VIEW FUSION FOR 3D HUMAN POSE ESTIMATION
    Zhang, Lijun
    Zhou, Kangkang
    Liu, Liangchen
    Li, Zhenghao
    Zhao, Xunyi
    Zhou, Xiang-Dong
    Shi, Yu
    2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2023, : 1600 - 1604
  • [10] Multi-view 3D Human Pose Estimation in Complex Environment
    Hofmann, M.
    Gavrila, D. M.
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2012, 96 (01) : 103 - 124