## 1 Introduction
### Related Works  
In the field of computer vision, the research on digitizing real-world scenes has received significant interest in the past decades due to the potential applications to daily life. For example, with the advance of virtual and augmented reality techniques, real estate agents can digitize their properties into 3D models for online browsing to achieve the goal of offering customers an immersive experience of inspecting houses [12; 18]. Digital museums offer a large number of users with internet access the opportunity to visit them virtually anytime and anywhere [19]. Benefiting from the advance of low-cost RGB-D cameras, multiview video research based on mining and analyzing the vast number of 2D framesfor 3D model reconstruction has been greatly boosted [20; 21; 22]. Image-related 3D modeling techniques require expertise and are time-consuming; thus, they remain as challenging research problems. In computer vision, many approaches have been studied to recover the spatial layout of a scene by acquiring surrounding images under different viewpoints. For each pixel in an image, accurate depth information plays an import role in calculating the corresponding coordinates in a 3D space. However, the cost to acquire high-quality depth information is often very high. A variety of approaches, categorized into passive and active techniques, have been studied to obtain RGB-D data. Passive techniques, such as stereoscopic camera pairs, derive the depth from disparity between images captured from each camera, while active techniques use a range sensor to emit some kind of light to assist depth calculation. The latter are recognized to offer accurate depth information from 3D surfaces. Currently, light detection and ranging (LiDAR) is the main modality for acquiring RGB-D data. LiDAR systems can be further divided into two classes: scanner-less LiDAR and scanning LiDAR [23]. The former captures the entire scene with each laser or light pulse, whereas the latter uses a laser beam to capture the scene in the fashion of point-by-point scanning. Low cost Kinect-like time-of-light (ToF) cameras, which are used for many consumer-level RGB-D data acquisition, belong to the scanner-less LiDAR class. ToF cameras are widely used in many indoor scene modeling applications because their operation speed is quick for real-time applications. However, ToF cameras often produce inaccurate depth information and thus suffer from the missing depth problem, which requires more efficient and reliable algorithms to deal with [10; 24]. In particular, laser pulses in a consumer-level ToF camera often fail in accurate depth information calculation for outdoor 3D modeling. One of the approaches for depth calculation in outdoor environments is called structure-from-motion [25; 26], which automatically recover camera parameters and 3-D positions of feature points by tracking discriminative features in a sequence of RGB images. Under the assumption of an affine camera model, the well-known factorization algorithm [27] can efficiently estimate a stable 3-D scene. However, traditional structure-from-motion algorithms cannot compute reliable depth and camera parameters when the 3-D scene is not suitable for the affine camera model. Moreover, these approaches cannot estimate reliable depth maps and camera parameters from a small set of images. To deal with the difficulty, [PERSON] et al. reconstruct a 3-D model of an outdoor scene accurately by using a large number of input images captured from a moving RGB camera [28; 29]. Based on similar algorithms, commercial software for 3D scene modeling using an unpiloted drone equipped with a moving RGB camera is available [16]. A ToF camera arranges laser pulses for depth information calculation into a 2D array, so that depth information can be represented as a depth image. An RGB-D image frame, which depicts a single view of the target scene including both the color and the shape, is thus formed by aligning an RGB image with the corresponding depth image.