Real-Scale 3-D Reconstruction With Monocular Zoom Technology

被引:0
|
作者
Song, Jinao [1 ]
Li, Jie [1 ]
Fan, Hao [1 ]
Qi, Lin [1 ]
Zhang, Shu [1 ]
Chen, Yong [1 ]
Dong, Junyu [1 ]
机构
[1] Ocean Univ China, Dept Informat Sci & Technol, Qingdao 266000, Peoples R China
基金
中国国家自然科学基金;
关键词
Three-dimensional displays; Accuracy; Structure from motion; Design methodology; Cameras; Image restoration; Image reconstruction; Monocular zooming; optical flow; real-scale 3-D reconstruction;
D O I
10.1109/TIM.2024.3497052
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We propose a method that is able to use the monocular zoom technology for real-scale 3-D reconstruction of the scene. To reconstruct the scene, we take a sequence of zoomed-in and zoomed-out figures. First, we can estimate zoomed-in camera parameters using the known zoomed-out camera parameters, which avoids calibrating the camera parameters twice. Then, we use the structure from motion (SfM) method (COLMAP) to reconstruct free-scale translations among these figures. After that, as we have pairs of zoom frames in the same scene, we can calculate the true scale of the scene by comparing the ratio between the free-scale translation of a pair of zoom frames and the difference in zoomed-out and the zoomed-in focal length. Finally, we use RAFT-stereo to compute the depth of the scene. In detail, we select two adjacent figures taken at the same focal length, make a stereo correction for them, and remove the nonco-vision area of the corrected images. This way, we obtain a more accurate matching of these images and then get a dense real-scale 3-D reconstruction. Experimental results have demonstrated that our method achieves good performance on monocular 3-D reconstruction with the real scale.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Shape Basis Interpretation for Monocular Deformable 3-D Reconstruction
    Agudo, Antonio
    Moreno-Noguer, Francesc
    IEEE TRANSACTIONS ON MULTIMEDIA, 2019, 21 (04) : 821 - 834
  • [2] A High-Performance Learning-Based Framework for Monocular 3-D Point Cloud Reconstruction
    Zamani, AmirHossein
    Ghaffari, Kamran
    Aghdam, Amir G.
    IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION, 2024, 8 : 695 - 712
  • [3] Weakly Supervised 3-D Building Reconstruction From Monocular Remote Sensing Images
    Li, Weijia
    Hu, Zhenghao
    Meng, Lingxuan
    Wang, Jinwang
    Zheng, Juepeng
    Dong, Runmin
    He, Conghui
    Xia, Gui-Song
    Fu, Haohuan
    Lin, Dahua
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 15
  • [4] Semantic Shape and Trajectory Reconstruction for Monocular Cooperative 3D Object Detection
    Cserni, Marton
    Rovid, Andras
    IEEE ACCESS, 2024, 12 : 167153 - 167167
  • [5] Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene
    Kumar, Suryansh
    Dai, Yuchao
    Li, Hongdong
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (05) : 1705 - 1717
  • [6] 3-D Semantic Terrain Reconstruction of Monocular Close-Up Images of Martian Terrains
    Tian, Pengzhi
    Yao, Meibao
    Xiao, Xueming
    Zheng, Bo
    Cao, Tao
    Xi, Yurong
    Liu, Haiqiang
    Cui, Hutao
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 16
  • [7] MonoPSTR: Monocular 3-D Object Detection With Dynamic Position and Scale-Aware Transformer
    Yang, Fan
    He, Xuan
    Chen, Wenrui
    Zhou, Pengjie
    Li, Zhiyong
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 1
  • [8] Whole Stomach 3D Reconstruction and Frame Localization From Monocular Endoscope Video
    Widya, Aji Resindra
    Monno, Yusuke
    Okutomi, Masatoshi
    Suzuki, Sho
    Gotoda, Takuji
    Miki, Kenji
    IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, 2019, 7
  • [9] 3-D LiDAR and Monocular Camera Calibration: A Review
    Zhang, Haoxin
    Li, Shuaixin
    Zhu, Xiaozhou
    Chen, Hongbo
    Yao, Wen
    IEEE SENSORS JOURNAL, 2025, 25 (07) : 10530 - 10555
  • [10] Scene Target 3D Point Cloud Reconstruction Technology Combining Monocular Focus Stack and Deep Learning
    Hu, Yanzhu
    Wang, Yingjian
    Wang, Song
    IEEE ACCESS, 2020, 8 : 168099 - 168110