  Active stereo technique using single pattern projection, a.k.a. one-shot 3D
scan, have drawn a wide attention from industry, medical purposes, etc. One
severe drawback of one-shot 3D scan is sparse reconstruction. In addition,
since spatial pattern becomes complicated for the purpose of efficient
embedding, it is easily affected by noise, which results in unstable decoding.
To solve the problems, we propose a pixel-wise interpolation technique for
one-shot scan, which is applicable to any types of static pattern if the
pattern is regular and periodic. This is achieved by U-net which is pre-trained
by CG with efficient data augmentation algorithm. In the paper, to further
overcome the decoding instability, we propose a robust correspondence finding
algorithm based on Markov random field (MRF) optimization. We also propose a
shape refinement algorithm based on b-spline and Gaussian kernel interpolation
using explicitly detected laser curves. Experiments are conducted to show the
effectiveness of the proposed method using real data with strong noises and
textures.
