Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan

TL;DR

This paper introduces a CNN-based pixel-wise phase estimation method and an MRF-based phase unwrapping technique to enhance the accuracy and stability of one-shot 3D scanning, especially under noisy conditions.

Contribution

It presents a novel pixel-wise interpolation approach using U-net for one-shot 3D scan reconstruction and a robust MRF-based phase unwrapping algorithm for improved decoding stability.

Findings

Effective in noisy and textured environments

Improves reconstruction accuracy and stability

Demonstrates robustness with real data

Abstract

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.