  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.
