A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms
This work addresses the need for a comparative study to guide method selection in 3D registration, particularly for indoor scanning applications, but it is incremental as it synthesizes existing approaches rather than introducing new ones.
The paper tackles the problem of 3D alignment for indoor scene reconstruction using RGB-D data, evaluating various methods across key steps like key point detection and pose estimation to identify optimal combinations for more accurate and complete reconstructions with cheap depth cameras.
3D alignment has become a very important part of 3D scanning technology. For instance, we can divide the alignment process into four steps: key point detection, key point description, initial pose estimation, and alignment refinement. Researchers have contributed several approaches to the literature for each step, which suggests a natural need for a comparative study for an educated more appropriate choice. In this work, we propose a description and an evaluation of the different methods used for 3D registration with special focus on RGB-D data to find the best combinations that permit a complete and more accurate 3D reconstruction of indoor scenes with cheap depth cameras.