  Deep neural network (DNN) architectures have been shown to outperform
traditional pipelines for object segmentation and pose estimation using RGBD
data, but the performance of these DNN pipelines is directly tied to how
representative the training data is of the true data. Hence a key requirement
for employing these methods in practice is to have a large set of labeled data
for your specific robotic manipulation task, a requirement that is not
generally satisfied by existing datasets. In this paper we develop a pipeline
to rapidly generate high quality RGBD data with pixelwise labels and object
poses. We use an RGBD camera to collect video of a scene from multiple
viewpoints and leverage existing reconstruction techniques to produce a 3D
dense reconstruction. We label the 3D reconstruction using a human assisted
ICP-fitting of object meshes. By reprojecting the results of labeling the 3D
scene we can produce labels for each RGBD image of the scene. This pipeline
enabled us to collect over 1,000,000 labeled object instances in just a few
days. We use this dataset to answer questions related to how much training data
is required, and of what quality the data must be, to achieve high performance
from a DNN architecture.
