Image-based 3D scene reconstruction and exploration in augmented reality

Yang, Ming‐Der; Chao, Chih-Fan; Huang, Kai-Siang; Liangyou, Lu; Chen, Yiping

doi:10.1016/j.autcon.2012.09.017

Cited by 113 publications

(54 citation statements)

References 14 publications

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“…Subsequently, the SfM-generated weakly supported surfaces with a low point cloud density were reinforced using multiview reconstruction software, CMPMVS, to complete 3D model reconstructions. In brief, IBM can be divided into three major steps, namely SIFT, SfM, and CMPMVS; detailed procedures were provided by Yang et al [42].…”

Section: Image-based Modelingmentioning

confidence: 99%

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

et al. 2017

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Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.

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Section: Image-based Modelingmentioning

confidence: 99%

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

et al. 2017

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“…Several studies have been conducted recently on the use of daily construction site photographs taken by common digital cameras for image-based modeling (IBM) such as Golparvar-Fard et al 2009a, 2011a, Bae et al 2013, and Jadidi et al 2014. These models are then superimposed on 4D as-planned models or Building Information Models (BIMs) to visualize progress deviations and/or to automate construction progress monitoring (Bae et al 2013;Golparvar-Fard et al 2009a;2011a;Kamat et al 2010;Yang et al 2013). McCoy et al 2014 investigated the use and access of IBM techniques to reduce uncertainty in construction planning for remote construction sites.…”

Section: Ibm In Constructionmentioning

confidence: 99%

“…This paper does not report a detailed discussion of the computer vision techniques and the related algorithms used in previous studies and in the current study. We encourage readers to check Snavely et al 2008;, Golparvar-Fard et al 2009b, and Yang et al 2013. The computer vision techniques used in this study are applied using the VisualSfM software (Wu 2011;Wu et al 2011).…”

Section: Ibm In Constructionmentioning

confidence: 99%

A step-by-step construction site photography procedure to enhance the efficiency of as-built data visualization: a case study

et al. 2015

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Introduction: Visualization of as-built data may change the future of construction project management to a more efficient area of knowledge if appropriate and easy photography and reconstruction tools would be developed and used by practitioners. Most of the current reconstructed 3D point cloud models use unordered photograph collections to generate 4D as-built models. Some of these photographs are not used in the final model mostly because of either a possible overlap with other photos or some faults in photography procedure. Computation time increases exponentially as the number of photos in a photo collection increases. Therefore, the unstructured processes may reduce the performance of a point-cloud model representation. This work shows how the available application of unordered photograph collections are regularly inefficient by measuring the performance of some important criteria, such as the registration success score and the computation time.Case description: The case study is the construction of a gas compressor station. Such industrial projects involve several building and work areas (e.g., substation, control building, and piping area). The construction site covers approximately 20 hectares. The case study was conducted in two stages. In the first stage, preexisting images have been used for image based modelling (IBM). In the second stage, images captured based on a step-by-step photography procedure (SPP) have been used for IBM. Discussion and evaluation:IBM performance in the first stage of the case study has been compared with the performance of the second stage by comparing the registration success scores. The IBM in the first stage of the case study results in sparse models, which hardly show the geometry of construction scenes. By contrast, capturing images based on the SPP in the second stage of the case study significantly changed the performance of IBM and increased the registration success score. Conclusion:This study provides an easy applicable on-site photography procedure. By adopting the proposed approach and by training the photographers, the model would be more desirable for application in more construction projects. The application of the SPP in the case study shows a significant improvement in the final reconstructed 3D point cloud model and as-built data visualization criteria.

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“…Recently, the development of automated construction systems on construction sites for safety and convenience has attracted interest [1,2,3]. In order to operate an automated construction system, it is essential to understand the environment in which construction equipment operates, and the first step of environment recognition is terrain reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust 3D Terrain Surface Reconstruction of Construction site Using Stereo camera

Sung¹,

Lee²,

Kwon³

et al. 2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-This paper presents a fast and robust threedimensional (3D) terrain surface reconstruction system using a stereo camera. The local featurebased 3D terrain surface reconstruction algorithm consists of two major steps: matching and terrain surface reconstruction. In this paper, extracted corners are described by multi-scale descriptors (MSDs), and the matching precision is increased by a quadratic interpolation method. In the terrain surface reconstruction step, the construction terrain surface is modeled using a 3D regular grid plane for its computational efficiency and robustness. The precision of the 3D regular grid plane is improved by inferring the 3D grid vertices from a robustly matched nearest point cloud. The computational complexity of the proposed terrain surface is simplified using 2D and 3D triangle mesh structures.In order to evaluate the proposed method, different types of 3D reconstruction methods were compared under the conditions of a practical construction site. The results of the experiment show that the MSD-based 3D terrain surface algorithm performed well in terms of robustness and computational efficiency.

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Image-based 3D scene reconstruction and exploration in augmented reality

Cited by 113 publications

References 14 publications

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

A step-by-step construction site photography procedure to enhance the efficiency of as-built data visualization: a case study

Fast and Robust 3D Terrain Surface Reconstruction of Construction site Using Stereo camera

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