A dense one-shot scanning technique that is robust to subsurface scattering is proposed. In this technique, a novel pattern, consisting of multiple parallel dotted lines and solid lines, that are aligned alternately, is proposed. To project such a pattern efficiently, a single-wavelength laser-based pattern projector is developed. To detect patterns robustly from captured images, a black and white camera attached with a narrow-band-path filter is used in conjunction with our novel deep learning based algorithm, which is based on a convolutional neural network (CNN). Because the detected lines must be identified for shape reconstruction, we apply a gap-coding technique, which is originally based on a grid-line pattern, to the dot pattern. To this end, we introduce a virtual grid-line structure, which is generated from the dot pattern. Additionally, we propose a calibration algorithm specialized for our system, where the pattern is static and shared with the shape reconstruction algorithm, i. e., correspondence problem remains. For a solution, gap-coding is further applied to find correspondences under epipolar constraints. The experimental results of scanning real objects are presented to demonstrate the effectiveness of our calibration and reconstruction techniques.