There is an increasing demand for 3D reconstruction of large scenes in areas such as urban planning, autonomous driving, virtual reality and gaming. Currently, considering data sources, the 3D reconstruction methods can be divided into two types: laser scanning-based and image-based [1,2]. Generally, the laser scanning-based model is most costly, and the reconstructed model suffers from a lack of texture. Image-based 3D reconstruction methods, on the other hand, are less expensive and more effective even with only a monocular camera [3]. Many fine-grained reconstruction efforts are carried out using image-based 3D reconstruction methods. These works mainly revolve around the SFM (Structure from Motion) and MVS (Multi-View Stereo) theories [4,5], observing the target from different perspectives and capturing the 3D information to obtain better reconstruction results. The core idea of the image-based 3D reconstruction method is the efficient use of geometric information on multi-view images [6,7,8].

To obtain high-quality models, current research focuses on two aspects. One is to improve the accuracy and computational performance of reconstruction algorithms. There are already many mature algorithms and software that can achieve high-quality image 3D reconstruction. Open-source algorithms include Colmap, OpenMVG, VisualSfM, etc., while commercial software includes Context Capture, MetaShape, Reality Capture, Pix4D, etc. In addition, some studies such as MVSNet [9] and R-MVSNet [10] have used an end-to-end depth estimation framework based on deep learning to obtain 3D dense point clouds by estimating depth directly from images to improve accuracy in scenes with repeated or missing textures and drastic changes in illumination. The reconstruction algorithms have been maturely developed relatively. The other is to improve the quality of the reconstructed model by acquiring or selecting high-quality images. Unlike the optimization of reconstruction algorithms, this is mainly applied in the data acquisition phase to input high-quality images to the reconstruction system. The quality of the images determines the reconstructed model quality [11], while the number and resolution of the images determine the time cost of the reconstruction process. Inadequate and insufficient coverage can result in mismatches between images or holes in reconstructed models. On the other hand, excessively redundant images would increase the time and calculation cost during image acquisition and reconstruction processes, and even lead to poor reconstruction quality [7,12]. Image collection is increasingly becoming an essential issue in 3D reconstruction work [13].

UAVs are widely used in the image acquisition process for 3D reconstruction to acquire images from multiple views, including different orientations and positions. However, most of the flight paths of UAVs are performed under manual control or some predefined flight modes in practical operations [14], in which situation a greater number of images is prone to be captured and thus cause redundancy and long-time consumption. An efficient path planning solution for UAVs to capture images autonomously which can ensure flight safety and reconstructability is urgently required [15,16]. This paper focuses on the planning of UAV photographic viewpoints and paths to achieve high-quality image acquisition and ultimately high-quality model reconstruction. The multi-rotor UAV with RTK (Real Time Kinematic) module and gimbal provides the hardware implementation basis for high-quality image collection [17,18,19], so that the one-to-one correspondence of captured images and the planned viewpoint can be met.

2. Priori Geometry Proxy

3. Viewpoint Optimization