Automatic registration of color images to 3D geometry

Yun-zhen, LI; Low, Kok-Lim

doi:10.1145/1629739.1629742

Cited by 10 publications

(12 citation statements)

References 24 publications

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“…In current related research work, reducing the impact of non-rigid distortion to the precision is only applied to simple scenes, such as close-range photogrammetry. Li et al [24] and Zheng et al [25], given the distance between the 3D feature point cloud and the LiDAR point cloud, established an error equation and added it to the bundle block adjustment for iterative calculation. They handled the problem of non-rigid distortion between the LiDAR point cloud and images, however, whether this method is suitable for large scenes such as aerial images still needs to be verified.…”

Section: Related Workmentioning

confidence: 99%

Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

Zheng

Sun³

et al. 2019

Remote Sensing

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VLS (Vehicle-borne Laser Scanning) can easily scan the road surface in the close range with high density. UAV (Unmanned Aerial Vehicle) can capture a wider range of ground images. Due to the complementary features of platforms of VLS and UAV, combining the two methods becomes a more effective method of data acquisition. In this paper, a non-rigid method for the aerotriangulation of UAV images assisted by a vehicle-borne light detection and ranging (LiDAR) point cloud is proposed, which greatly reduces the number of control points and improves the automation. We convert the LiDAR point cloud-assisted aerotriangulation into a registration problem between two point clouds, which does not require complicated feature extraction and match between point cloud and images. Compared with the iterative closest point (ICP) algorithm, this method can address the non-rigid image distortion with a more rigorous adjustment model and a higher accuracy of aerotriangulation. The experimental results show that the constraint of the LiDAR point cloud ensures the high accuracy of the aerotriangulation, even in the absence of control points. The root-mean-square error (RMSE) of the checkpoints on the x, y, and z axes are 0.118 m, 0.163 m, and 0.084m, respectively, which verifies the reliability of the proposed method. As a necessary condition for joint mapping, the research based on VLS and UAV images in uncontrolled circumstances will greatly improve the efficiency of joint mapping and reduce its cost.

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Section: Related Workmentioning

confidence: 99%

Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

Zheng

Sun³

et al. 2019

Remote Sensing

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“…Moreover, it will be possible to merge additional photographic datasets acquired in the future with different capture strategies. Similar to our approach, Li and Low [2009] apply SfM to an image set. However, their refinement step depends on the presence of artificially textured planes in the geometry, obtained by projecting special light patterns.…”

Section: :4mentioning

confidence: 99%

A Fast and Robust Framework for Semiautomatic and Automatic Registration of Photographs to 3D Geometry

Pintus

Gobbetti

2015

J. Comput. Cult. Herit.

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We present a simple, fast, and robust complete framework for 2D/3D registration capable to align in a semiautomatic or completely automatic manner a large set of unordered images to a massive point cloud. Our method converts the hard to solve image-to-geometry registration task in a Structure-from-Motion (SfM) plus a 3D/3D alignment problem. We exploit a SfM framework that, starting just from an unordered image collection, computes an estimate of the camera parameters and a sparse 3D geometry deriving from matched image features. We then coarsely register this model to the given 3D geometry by estimating a global scale and absolute orientation using two solutions: a minimal user intervention or a stochastic global point set registration approach. A specialized sparse bundle adjustment (SBA) step, that exploits the correspondence between the sparse geometry and the fine input 3D model, is then used to refine intrinsic and extrinsic parameters of each camera. Output data is suitable for photo blending frameworks to produce seamless colored models. The effectiveness of the method is demonstrated on a series of synthetic and real-world 2D/3D Cultural Heritage datasets. ACM Reference Format:Ruggero Pintus and Enrico Gobbetti. 2015. A fast and robust framework for semiautomatic and automatic registration of photographs to 3D geometry. ACM J. Comput. Cult.

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“…Due to a stereo pair of optical images can be used for 3D reconstruction by using photogrammetry techniques (Liu et al, 2006) and stereo vision (Sirmacek et al, 2013), the problem of image-to-point cloud registration can be changed into 3D-3D registration (Zhao et al, 2005). In this research direction, SIFT algorithm (Lowe, 2004;Böhm and Becker, 2007) is usually used for correspondent point extraction; and then 3D reconstruction is applied based on correspondent point pairs; last, ICP (Chen and Medioni, 1991;Besl and Mckay, 1992) is used for the registration of 3D dense point cloud from a pair of adjacent images and 3D LiDAR point cloud (Li and Low, 2009). However, these methods are complicated, and accuracy of 3D reconstruction is easy affected by wrong correspondent point pairs.…”

Section: Introductionmentioning

confidence: 99%

Fast Registration of Terrestrial Lidar Point Cloud and Sequence Images

Shao

Zhang

Zhu

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Image has rich color information, and it can help to promote recognition and classification of point cloud. The registration is an important step in the application of image and point cloud. In order to give the rich texture and color information for LiDAR point cloud, the paper researched a fast registration method of point cloud and sequence images based on the ground-based LiDAR system. First, calculating transformation matrix of one of sequence images based on 2D image and LiDAR point cloud; second, using the relationships of position and attitude information among multi-angle sequence images to calculate all transformation matrixes in the horizontal direction; last, completing the registration of point cloud and sequence images based on the collinear condition of image point, projective center and LiDAR point. The experimental results show that the method is simple and fast, and the stitching error between adjacent images is litter; meanwhile, the overall registration accuracy is high, and the method can be used in engineering application.

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Automatic registration of color images to 3D geometry

Cited by 10 publications

References 24 publications

Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

A Fast and Robust Framework for Semiautomatic and Automatic Registration of Photographs to 3D Geometry

Fast Registration of Terrestrial Lidar Point Cloud and Sequence Images

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