Combining SLAM with muti-spectral photometric stereo for real-time dense 3D reconstruction
This work addresses the need for real-time dense 3D reconstruction in SLAM applications, but it appears incremental as it integrates existing methods with a new fusion approach.
The paper tackled the problem of achieving dense 3D reconstruction with low computational cost by proposing a framework that combines semi-dense SLAM with multispectral photometric stereo, resulting in effectively denser 3D reconstructions as shown in experiments.
Obtaining dense 3D reconstrution with low computational cost is one of the important goals in the field of SLAM. In this paper we propose a dense 3D reconstruction framework from monocular multispectral video sequences using jointly semi-dense SLAM and Multispectral Photometric Stereo approaches. Starting from multispectral video, SALM (a) reconstructs a semi-dense 3D shape that will be densified;(b) recovers relative sparse depth map that is then fed as prioris into optimization-based multispectral photometric stereo for a more accurate dense surface normal recovery;(c)obtains camera pose that is subsequently used for conversion of view in the process of fusion where we combine the relative sparse point cloud with the dense surface normal using the automated cross-scale fusion method proposed in this paper to get a dense point cloud with subtle texture information. Experiments show that our method can effectively obtain denser 3D reconstructions.