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Fusion of thermal imagery with point clouds for building facade thermal attribute mapping

机译:热量图像与点云的融合,用于建筑外观散热映射

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Thermal image data are widely used to assess the insulation quality of buildings and to detect thermal leakages. In our approach, we merge terrestrial thermal image data and 3D point clouds to perform thermal texture mapping for building facades. Since geo-referencing data of a hand-held thermal camera is usually not available in such applications, registration between thermal images and a 3D point cloud (for instance generated from RGB image data by structure-from-motion techniques) is essential. In our approach, thermal image data registration is conducted in four steps: First, another point cloud is generated from the thermal image data. Next, a coarse registration between thermal point cloud and RGB point cloud is performed using the fast global registration (FGR) algorithm. The best corresponding thermal-RGB image pairs are acquired by picking up the lowest Euclidean distance between the exterior orientation parameters of thermal images and transformed exterior orientation parameters of RGB images. Subsequently, radiation-invariant feature transform (RIFT), normalized barycentric coordinate system (NBCS) and random sample consensus (RANSAC) are employed to extract reliable matching features on thermal-RGB image pairs. Afterwards, a fine registration is performed by mono-plotting of the RGB image, followed by image resection of the thermal image. Finally, in terms of texture mapping algorithms, in order to remove the blur effects caused by small misalignments for different candidate images, a global image pose refinement approach, which aims to minimize the temperature disagreements provided by different images for the same object points, is proposed. In addition, in order to ensure high geometric and radiant accuracy, camera calibrations are performed. Experiments showed that the proposed method could not only achieve high geometric registration accuracy, but also provide a good radiometric accuracy with RMSE lower than 1.5 degrees C.
机译:热图像数据广泛用于评估建筑物的绝缘质量并检测热泄漏。在我们的方法中,我们合并地面热图像数据和3D点云来对建筑物外观进行热纹理映射。由于手持热相机的地理参考数据通常不可用,因此在这种应用中不可用,因此热图像和3D点云之间的登记(例如通过结构 - 型从运动技术从RGB图像数据产生)是必要的。在我们的方法中,热图像数据配准在四个步骤:首先,从热图像数据产生另一点云。接下来,使用快速全局注册(FGR)算法来执行热点云和RGB点云之间的粗略登记。通过拾取热图像的外向方向参数与RGB图像的转换外向参数之间的最低欧几里德距离来获取最佳相应的热RGB图像对。随后,采用辐射不变特征变换(RIFT),归一化的重心坐标系(NBC)和随机样本共识(RANSAC)来提取热RGB图像对上的可靠匹配特征。然后,通过单绘制RGB图像来执行精细的注册,然后进行热图像的图像切除。最后,在纹理映射算法方面,为了去除由不同候选图像的小错位引起的模糊效应,全局图像姿势细化方法,其旨在最小化由不同图像提供的不同图像提供的温度分歧是建议的。另外,为了确保高几何和辐射精度,执行相机校准。实验表明,该方法不仅可以达到高几何配准精度,还可以提供良好的辐射精度,RMSE低于1.5℃。

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