SfM/SLAM and 3D Reconstruction: Trends, Prospects and Applications in Space

Dear Colleagues,

Recovering 3D structures and 6D poses of objects from a single image or sequence of images is an active topic attracting researchers from fields such as 3D computer vision, object pose estimation, and artificial intelligence (AI), etc. Recently, enabled by the development of AI, we have witnessed tremendous progress in 3D reconstruction, especially in structure from motion (SfM), multi-view stereo (MVS), Simultaneous Localization and Mapping (SLAM), point-cloud registration, and object pose estimation. In addition, 3D reconstruction demonstrates perspective potential in the application in space, such as satellite pose estimation and reconstruction. Nevertheless, there are still many challenges to be solved, including 3D reconstruction and camera self-calibration from sparse views, low-quality images, and surfaces with no textures or repeating textures, reliable 2D–2D, 2D–3D, or 3D–3D feature extraction, matching and tracking across views, and generalizable pose estimation. Furthermore, the absence of large-scale datasets in some specific fields, such as in space, also negatively impacts the application of 3D reconstruction.

The main aim of this Special Issue is to seek original contributions that address the above challenges or highlight emerging applications in 3D reconstruction. The topics of interest include, but are not limited to:

• SfM from sparse views;
• Camera self-calibration from a single/two view(s);
• Indoor or outdoor SLAM;
• Passive/active 3D reconstruction;
• Implicit/explicit representation-based 3D reconstruction (e.g. Nerf, GS, etc.)
• 3D reconstruction from difficult surfaces;
• 2D–2D, 2D–3D, or 3D–3D feature matching and tracking;
• 3D point-cloud registration, segmentation;
• 6D object pose estimation or tracking;
• Uncertainty estimation;
• Novel 3D reconstruction and pose estimation in space.

Dr. Zhaoshuai Qi
Dr. Junhui Huang
Dr. Qi Xue
Guest Editors

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