Super-Resolution Techniques in Photogrammetric 3D Reconstruction from Close-Range UAV Imagery

Abstract

Current Multi-View Stereo (MVS) algorithms are tools for high-quality 3D model reconstruction, strongly depending on image spatial resolution. In this context, the combination of image Super-Resolution (SR) with image-based 3D reconstruction is turning into an interesting research topic in photogrammetry, around which however only a few works have been reported so far in the literature. Here, a thorough study is carried out on various state-of-the-art image SR techniques to evaluate the suitability of such an approach in terms of its inclusion in the 3D reconstruction process. Deep-learning techniques are tested here on a UAV image dataset, while the MVS task is then performed via the Agisoft Metashape photogrammetric tool. The data under experimentation are oblique cultural heritage imagery. According to results, point clouds from low-resolution images present quality inferior to those from upsampled high-resolution ones. The SR techniques HAT and DRLN outperform bicubic interpolation, yielding high precision/recall scores for the differences of reconstructed 3D point clouds from the reference surface. The current study indicates spatial image resolution increased by SR techniques may indeed be advantageous for state-of-the art photogrammetric 3D reconstruction.

Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique

“…These include both those can be directly used [30][31][32] and those that have been created by researchers specifically for relevant studies [26,33]. While satellite images are most commonly favored as remotely sensed images for resolution improvement, aerial photographs obtained by platforms such as UAVs and aircraft have been used as well [34]. A common element of these datasets that distinguishes them from others is their focus on solving real-world problems.…”

The Extraction of Roof Feature Lines of Traditional Chinese Village Buildings Based on UAV Dense Matching Point Cloud

“…As a result of the rapid development of dense matching technology and aerial triangulation methods, full-automatic photogrammetric processing of nadir and oblique imagery acquired from low altitude unmanned aerial vehicles (UAVs) has become increasingly precise and intelligent [2]. It plays an instrumental role in various fields including but not limited to 3D reconstruction [3] and point cloud segmentation [4]. Point cloud generated through Multi-view Stereo (MVS) have also become a significant source of 3D geographic information acquisition [5].…”