  Recently, 3D version has been improved greatly due to the development of deep
neural networks. A high quality dataset is important to the deep learning
method. Existing datasets for 3D vision has been constructed, such as Bigbird
and YCB. However, the depth sensors used to make these datasets are out of
date, which made the resolution and accuracy of the datasets cannot full fill
the higher standards of demand. Although the equipment and technology got
better, but no one was trying to collect new and better dataset. Here we are
trying to fill that gap. To this end, we propose a new method for object
reconstruction, which takes into account the speed, accuracy and robustness.
Our method could be used to produce large dataset with better and more accurate
annotation. More importantly, our data is more close to the rendering data,
which shrinking the gap between the real data and synthetic data further.
