Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences.

Hoegner, L.; Stilla, Uwe

doi:10.21611/qirt.2016.042

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…A common method for texture reconstruction is the blending-based method where images are projected on the surface of the model following its intrinsic and extrinsic camera parameters and in the end mixing all the images into a final texture [108][109][110][111][112]. The downsides of this method are the high noise sensitivity, the appearance of blurring and ghosting in situations where camera poses are inaccurate which can be due to distortion or geometric errors in in-depth map computation, or accumulation of residual camera pose leading to camera shift when computing the trajectory of the camera.…”

Section: Texturing Reconstructionmentioning

confidence: 99%

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

Vong

Matos-Carvalho

Toffanin³

et al. 2021

Remote Sensing

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The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.

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Section: Texturing Reconstructionmentioning

confidence: 99%

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

Vong

Matos-Carvalho

Toffanin³

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Blending-based method: It is the simplest method of texture mapping. Bi et al [7], Callieri et al [21], and Hoegner et al [22] projected the captured images onto the surface of the geometric model according to the intrinsic and extrinsic orientation parameters of the camera and then merged all the images to obtain the final texture image. This method is suitable for processing close-range and small-range models, such as indoor scenes and small objects.…”

Section: D Texture Reconstructionmentioning

confidence: 99%

A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models

Xiao

Guo

et al. 2020

Remote Sensing

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The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.

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“…There are two measuring points on the exterior window and wall of each orientation, and an aluminum foil measuring point is added on the south facade (the spatial resolution of the thermal infrared image is 3.92 cm, and the size of the aluminum foil plate is 50 × 50 cm, while its location can be accurately found on the image). Due to the low emissivity of aluminum foil (strong alumina 0.2), its own radiation amount is far less than the reflection amount of radiation to the surrounding environment, so aluminum foil is often used to determine the environmental emission temperature in many experiments [16,28,29]. In this paper, the aluminum foil is not only used as a mark point, There are two measuring points on the exterior window and wall of each orientation, and an aluminum foil measuring point is added on the south facade (the spatial resolution of the thermal infrared image is 3.92 cm, and the size of the aluminum foil plate is 50 × 50 cm, while its location can be accurately found on the image).…”

Section: Validationmentioning

confidence: 99%

“…Therefore, the process of feature points extraction and matching becomes more difficult on TIR images than with visual images. The most commonly used 3D reconstruction is based on the Structure from Motion algorithm (SFM), which calculates 3D information from the time series of 2D images [15,16]. The main process of the SFM algorithm includes feature extraction and matching (SIFT feature detection, SURF feature detection, ORB feature detection, AKAZE feature detection), sparse point cloud reconstruction, dense point cloud reconstruction, and surface reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A Thermal Performance Detection Method for Building Envelope Based on 3D Model Generated by UAV Thermal Imagery

et al. 2020

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The evaluation and renovation of existing building envelope has important practical significance for energy conservation and emission reduction in the field of architecture. With the development of digital cities, 3D models with rich temperature information can realize the comprehensive and accurate detection and evaluation of the existing building envelope. However, the 3D model reconstructed from thermal infrared images has only relative temperature distribution and no temperature value of each location, so it is impossible to quantify the extent of the defect from it. To solve this issue, this paper develops a method to establish a 3D point cloud model with temperature information at selected points. The proposed 3D model is generated based on the thermal infrared images acquired by an unmanned aerial vehicle (UAV) equipped with an infrared camera. In the generated 3D thermal infrared model, we can not only get the relative temperature distribution of the building’s full envelope structure, but also obtain the exact temperature value of any selected point. This method has been verified by field measurements and the result shows that the deviation is within 5 °C. In addition to temperature information, the generated 3D model also has spatial and depth information, which can reflect the appearance information and 3D structure of the monitoring target more realistically. Thus, by using this method, it is possible to achieve a comprehensive, accurate, and efficient on-site assessment of the building envelope in the urban area.

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Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences.

Cited by 8 publications

References 26 publications

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models

A Thermal Performance Detection Method for Building Envelope Based on 3D Model Generated by UAV Thermal Imagery

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