  Deep Neural Networks (DNNs) require large amounts of annotated training data
for a good performance. Often this data is generated using manual labeling
(error-prone and time-consuming) or rendering (requiring geometry and material
information). Both approaches make it difficult or uneconomic to apply them to
many small-scale applications. A fast and straightforward approach of acquiring
the necessary training data would allow the adoption of deep learning to even
the smallest of applications. Chroma keying is the process of replacing a color
(usually blue or green) with another background. Instead of chroma keying, we
propose luminance keying for fast and straightforward training image
acquisition. We deploy a black screen with high light absorption (99.99\%) to
record roughly 1-minute long videos of our target objects, circumventing
typical problems of chroma keying, such as color bleeding or color overlap
between background color and object color. Next we automatically mask our
objects using simple brightness thresholding, saving the need for manual
annotation. Finally, we automatically place the objects on random backgrounds
and train a 2D object detector. We do extensive evaluation of the performance
on the widely-used YCB-V object set and compare favourably to other
conventional techniques such as rendering, without needing 3D meshes, materials
or any other information of our target objects and in a fraction of the time
needed for other approaches. Our work demonstrates highly accurate training
data acquisition allowing to start training state-of-the-art networks within
minutes.
