Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

doi:10.1117/1.oe.54.3.031105

Cited by 12 publications

(12 citation statements)

References 32 publications

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“…2D-3D registration between pairs of modalities such as photos to LIDAR [27], [28], spherical images to LIDAR [29], and images to range sensor [30], [31] have also been investigated. In our preliminary researches, 3D feature descriptors were tested and their performance on multi-modal registration in various domains was analysed [32], [33].…”

Section: B Multi-modal Data Registrationmentioning

confidence: 99%

Big Data Analysis for Media Production

et al. 2016

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Abstract-A typical high-end film production generates several terabytes of data per day, either as footage from multiple cameras or as background information regarding the set (laser scans, spherical captures, etc). This paper presents solutions to improve the integration of the multiple data sources, and understand their quality and content, which are useful both to support creative decisions on-set (or near it) and enhance the post-production process. The main cinema specific contributions, tested on a multisource production dataset made publicly available for research purposes, are the monitoring and quality assurance of multi-camera set-ups, multisource registration and acceleration of 3D reconstruction, anthropocentric visual analysis techniques for semantic content annotation, and integrated 2D-3D web visualization tools. We discuss as well improvements carried out in basic techniques for acceleration, clustering and visualization, which were necessary to deal with the very large multisource data, and can be applied to other big data problems in diverse application fields.

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Section: B Multi-modal Data Registrationmentioning

confidence: 99%

Big Data Analysis for Media Production

et al. 2016

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“…Multi-sensor image registration is a prerequisite for image fusion. Therefore, it has always been a research hotspot [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Non-Rigid Registration via Global to Local Transformation

Pan

Zhou

et al. 2020

Teh. vjesn.

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Non-rigid point set and image registration are key problems in plenty of computer vision and pattern recognition tasks. Typically, the non-rigid registration can be formulated as an optimization problem. However, registration accuracy is limited by local optimum. To solve this problem, we propose a method with global to local transformation for non-rigid point sets registration and it also can be used to infrared (IR) and visible (VIS) image registration. Firstly, an objective function based on Gaussian fields is designed to make a problem of non-rigid registration transform into an optimization problem. A global transformation model, which can describe the regular pattern of non-linear deformation between point sets, is then proposed to achieve coarse registration in global scale. Finally, with the results of coarse registration as initial value, a local transformation model is employed to implement fine registration by using local feature. Meanwhile, the optimal global and local transformation models estimated from edge points of IR and VIS image pairs are used to achieve non-rigid image registration. The qualitative and quantitative comparisons demonstrate that the proposed method has good performance under various types of distortions. Moreover, our method can also produce accurate results of IR and VIS image registration.

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“…Previous work has been done to register up to two full frame texel images, including methods that incorporate low-cost gps/ins measurements to improve the registration. 7 In order to have better registration, there should be enough overlap between images and using only two images will not produce a full 3D image. Recently a method was proposed to use texel swaths instead of a full texel image.…”

Section: Introductionmentioning

confidence: 99%

“…A final test is made that uses the fundamental matrix derived from the SOP matrix. 7 This process may further remove a few extreme outliers which are not consistent with both the models.…”

mentioning

confidence: 99%

Three-dimensional image reconstruction using bundle adjustment applied to multiple texel images

Khatiwada

Budge

2016

SPIE Proceedings

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The importance of creating 3D imagery is increasing and has many applications in the field of disaster response, digital elevation models, object recognition, and cultural heritage. Several methods have been proposed to register texel images, which consist of fused lidar and digital imagery. The previous methods were limited to registering up to two texel images or multiple texel swaths having only one strip of lidar data per swath. One area of focus still remains to register multiple texel images to create a 3D model. The process of creating true 3D images using multiple texel images is described. The texel camera fuses the 2D digital image and calibrated 3D lidar data to form a texel image. The images are then taken from several perspectives and registered. The advantage of using multiple full frame texel images over 3D-or 2D-only methods is that there will be better registration between images because of the overlapping 3D points as well as 2D texture used in the joint registration process. The individual position and rotation mapping to a common world coordinate frame is calculated for each image and optimized. The proposed methods incorporate bundle adjustment for jointly optimizing the registration of multiple images. Sparsity is exploited as there is a lack of interaction between parameters of different cameras. Examples of the 3D model are shown and analyzed for numerical accuracy

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Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

Cited by 12 publications

References 32 publications

Big Data Analysis for Media Production

Big Data Analysis for Media Production

Non-Rigid Registration via Global to Local Transformation

Three-dimensional image reconstruction using bundle adjustment applied to multiple texel images

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