  Stereo matching is an important problem in computer vision which has drawn
tremendous research attention for decades. Recent years, data-driven methods
with convolutional neural networks (CNNs) are continuously pushing stereo
matching to new heights. However, data-driven methods require large amount of
training data, which is not an easy task for real stereo data due to the
annotation difficulties of per-pixel ground-truth disparity. Though synthetic
dataset is proposed to fill the gaps of large data demand, the fine-tuning on
real dataset is still needed due to the domain variances between synthetic data
and real data. In this paper, we found that in synthetic datasets,
close-to-real-scene texture rendering is a key factor to boost up stereo
matching performance, while close-to-real-scene 3D modeling is less important.
We then propose semi-synthetic, an effective and fast way to synthesize large
amount of data with close-to-real-scene texture to minimize the gap between
synthetic data and real data. Extensive experiments demonstrate that models
trained with our proposed semi-synthetic datasets achieve significantly better
performance than with general synthetic datasets, especially on real data
benchmarks with limited training data. With further fine-tuning on the real
dataset, we also achieve SOTA performance on Middlebury and competitive results
on KITTI and ETH3D datasets.
