Scanning is a key element for many use cases in the Architectural, Engineering, Construction and Operation industry. It provides point clouds used for construction quality assurance, scan-to-BIM workflows and construction surveys. However, data acquisition using static laser scanners or photogrammetry methods is labour intensive during scanning and post-processing. Mobile scanners are conceptually the solution to this problem, given their potential to dramatically reduce on-site scanning effort and eliminate post-processing work. However, current mobile mapping devices are limited to producing point clouds of relatively low resolution. In this paper, we propose a dense 3D reconstruction pipeline for improving the resolution of point clouds, suitable for hand-held scanners comprised of a colour camera and a lidar. We fuse time-synchronized and spatially registered images and lidar sweeps using a deep learning method into dense scans of higher resolution, which are then used for progressive reconstruction. The novelty of our approach is that we first increase the precision and density of a bunch of individual lidar scans by inferring additional geometric constraints coming from predicted feature maps in the corresponding images. Then we automatically register these scans together, thus reconstructing the scene progressively in an odometric manner. We built a prototypic scanner, implemented our reconstruction pipeline as a software package and tested the whole in both indoor and outdoor case studies. The results showed that our method provided an overall noise reduction in point clouds by 11% and increased their density around 6 times.