Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoints and path planning for 3D reconstruction of large-scale objects. It presents a bibliography of more than 200 references to cover different aspects of the topic. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

This study addresses a view-path-planning problem during 3-D scanning of a large-scale structure based on multiview stereo (MVS) for unmanned aerial platforms. Recently, most studies have adopted an explore-then-exploit strategy for 3-D scanning. The strategy first generates a coarse model from a simple overhead scanning and then plans an inspection path to cover the entire surface of the coarse model. However, even though the inspection path may be optimal, it is difficult to guarantee a complete and accurate reconstruction result due to defective factors of MVS such as occlusions, textureless surfaces, and insufficient parallaxes. Furthermore, the entire procedure of this strategy is inefficiently slow because of path redundancies and long MVS processing time. Therefore, we propose a novel view-path-planning method for 3-D scanning based on an online MVS reconstruction algorithm. The suggested method incrementally reconstructs the target model online and iteratively plans view paths by analyzing the current partial reconstructions. The method continuously analyzes the quality of the model and detects inaccurately reconstructed surfaces. It then plans an inspection path that provides a complete coverage of the detected surfaces while maximizing the performance of MVS. This method can construct a complete 3-D model in a single scanning trial without the need for rescanning. Extensive experiments show that our method outperforms the other state-of-the-art methods, especially in terms of the model completeness of complex structures.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

This study addresses a depth-completion problem for sequential data to reconstruct 3D models of outdoor scenes. While many deep-learning-based approaches have recently achieved promising results, their results are not directly applicable to 3D modeling because of several reasons. First, most results contain a lot of outliers because of irregularly distributed sparse measurements. Second, they ignore temporal coherence in sequential frames and produce temporally inconsistent depths. Therefore, we propose a new method that predicts temporally consistent depths with corresponding confidences from sequential frames. The suggested method can efficiently remove the outliers based on confidence estimates, which accurately represent the true prediction errors. The method also produces temporally consistent depths by integrating the depth information of consecutive frames. In addition, we present a 3D-modeling system that reconstructs a globally consistent 3D model in real-time using the results from the proposed depth completion method. Extensive experiments on synthetic and real-world datasets show that our method outperforms the other state-of-the-art methods in terms of both depth-completion and 3D-modeling accuracies.