## 1 Introduction  
### Motivation  
In the field of three-dimensional (3D) computer vision, researchers aim at quickly reconstructing 3D models from an image sequence due to its potential applications in robotics, augmented reality (AR), geodesy, remote sensing, 3D face recognition, drone or vehicle navigation, and 3D printing. Researchers in remote sensing provide two traditional 3D reconstruction techniques including airborne image photogrammetry [1] and light detection and ranging (LiDAR) [2]. Both approaches reconstruct high quality 3D models; however, their acquisition cost is very high. With the advances of low-cost 3D data acquisition devices, researchers in computer vision have introduced many image-based 3D modeling techniques such as simultaneous location and mapping (SLAM) [3], multiview stereo (MVS) [4; 5; 6], photo tourism [7], virtual reality modeling [8], and an RGB-D video-based method [9]. These methods can represent a real-world scene as a point cloud which consists of a large number of points distributed in a 3D space to faithfully represent the intrinsic structure of the scene. A point cloud representation can be used for 3D inspection as it renders detailed 3D environments accurately. The cost of consumer-level color and depth (RGB-D) cameras, e.g., Microsoft Kinect, is low and thus widely used to reconstruct 3D indoor scenes [10]. However, Kinect-like scanning devices fail in capturing reliable depth images from outdoor scenes. On the other hand, high resolution RGB cameras in an unmanned drone can faithfully capture the surrounding images of a real-world scene, which can be used to build up the corresponding point cloud if the intrinsic and external camera parameters are carefully calibrated [11]. To reconstruct a 3D scene model from an RGB video requires an accurate pose estimation algorithm which augments the input video with an additional depth channel and transforms each frame into an RGB-D image. Putting all of these together, an image-based scene model can be reconstructed from an RGB video captured by a moving camera. Image-based 3D scene modeling is predicated on the assumption that the observed imagery is generated by joining multiple views of a 3D scene. Recently, with the depth or combined color and depth imagery from a low-cost Kinect-like sensor as input data, state-of-the-art approaches focus on the development of novel methods that can accurately reconstruct a 3D scene model from a multiview RGB-D video. The template-based approach is an efficient method that transforms both depth and color information in a limited set of view-specific templates into a 3D point cloud which renders the appearance and geometric structures of a real-world scene [12]. Because the pose parameters of each template are given in the model reconstruction phase, it provides a coarse estimation of the pose parameters, i.e., the depth map and external camera parameters, of the input image in the surveillance phase. Once the viewpoint class of the input image is determined, the coarse estimation of pose parameters and depth map should be fine-tuned since location error exists in the viewpoint of capturing the image using a conventional GPS-based drone navigation scheme. The fine-tuned camera parameters and depth map of the current input RGB image are then used to compute the current view-specific point cloud which can keep the 3D model up-to-date if the alignment error between the current point cloud and the model point cloud is low. On the contrary, based on the assumption that the status of the target scene is stationary, an abnormal event occurring in the scene under surveillance is detected if the alignment error is large.