Complete Scene Reconstruction by Merging Images and Laser Scans

Gao, Xiang; Shen, Shuhan; Zhu, Longxiang; Shi, Tianxin; Wang, Zhiheng; Hu, Zhanyi

doi:10.1109/tcsvt.2019.2943892

Cited by 28 publications

(7 citation statements)

References 59 publications

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“…In the field of heritage protection, combining LiDAR data and matching point clouds also was being used to create a high-resolution 3D model of monuments, statues, or facades (Schenk and Csathó, 2002). In the large-scale and complicated architectural scene, the images and laser scans were merged with a coarse-to-fine strategy (Gao et al, 2020) for reconstructing an accurate and complete 3D model (point cloud or surface mesh). Although point clouds fusion has broad application scenarios, the completeness and accuracy of the matching point clouds and the LiDAR data have yet to be significantly improved individually.…”

Section: Point Clouds Fusionmentioning

confidence: 99%

“…The fusion methods of LiDAR data and image data can have different levels depending on the differences in the emphasis of the data sources, which include: 1) point clouds fusion, 2) LiDAR data interpolation aided by images, and 3) dense image matching constrained by LiDAR data. Point clouds fusion directly integrates the LiDAR data and the point clouds generated by dense image matching, which plays an important role in geoscience applications, such as Digital Surface Model (DSM) generation (Schenk and Csathó, 2002), urban 3D modeling (Gao et al, 2020;Mandlburger et al, 2017), and forest resource management (White et al, 2013). However, the completeness and accuracy of the matching point clouds and LiDAR data are not significantly improved individually.…”

Section: Introductionmentioning

confidence: 99%

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LiDAR-guided stereo matching with a spatial consistency constraint

Zhang

Zou

Liu

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Point Clouds Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LiDAR-guided stereo matching with a spatial consistency constraint

Zhang

Zou

Liu

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

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“…SfM technique has achieved great success in the past decades [1]- [3], [6], [13]- [16]. The general pipeline of SfM contains two major stages: the matching stage and the reconstruction stage.…”

Section: Related Workmentioning

confidence: 99%

Efficient Covisibility-based Image Matching for Large-Scale SfM

Bao

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Structure-from-Motion is a technology used to obtain scene structure through image collection, which is a fundamental problem in computer vision. For unordered Internet images, SfM is very slow due to the lack of prior knowledge about image overlap. For sequential images, knowing the large overlap between adjacent frames, SfM can adopt a variety of acceleration strategies, which are only applicable to sequential data. To further improve the reconstruction efficiency and break the gap of strategies between these two kinds of data, this paper presents an efficient covisibility-based incremental SfM. Different from previous methods, we exploit covisibility and registration dependency to describe the image connection which is suitable to any kind of data. Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. Experiments on the unordered images and mixed data verify the effectiveness of the proposed method, which is three times faster than the state of the art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy. The source code is publicly available at https://github.com/openxrlab/xrsfm

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“…The 3D reconstruction based on point clouds has been studied by many researchers, which mainly falls into two categories. The first category uses a precision instrument [15] (such as a laser detector) to directly measure the 3D coordinates of the point on the surface of the target object. However, its applications are restricted owing to high cost.…”

Section: Introductionmentioning

confidence: 99%

Camera Self-Calibration and 3D Stratified Reconstruction for Image Sequences with Geometric Features

Jiang,

Zhao,

Zhuo

et al. 2024

Preprint

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Three-dimensional (3D) stratified reconstruction under geometric transformations is of great significance in computer vision research. Certain applications involve projective or affine reconstruction; however, metric reconstruction best reflects the factual information of the object. The internal and external parameters of the camera play important roles in the stratified reconstruction. This study performed camera calibration using the geometric constraints of a scene in an image sequence or video stream. Consequently, 3D stratified reconstruction from the point cloud was performed according to the algebraic and geometric relations between projective, affine, and metric transformations. Delaunay triangulation and texture mapping were then used to restore the surface of the object in the scene. The results show that the object could be reconstructed both in disordered images and video streams and could be used to restore the 3D appearance of the object and obtain its corresponding geometric information. Finally, the You Only Look Once Version (YOLOV5) target detection algorithm was used to detect the metric reconstruction results, which demonstrated that the effect was satisfactory.

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Complete Scene Reconstruction by Merging Images and Laser Scans

Cited by 28 publications

References 59 publications

LiDAR-guided stereo matching with a spatial consistency constraint

LiDAR-guided stereo matching with a spatial consistency constraint

Efficient Covisibility-based Image Matching for Large-Scale SfM

Camera Self-Calibration and 3D Stratified Reconstruction for Image Sequences with Geometric Features

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