  We introduce BIDCD -- the Bosch Industrial Depth Completion Dataset. BIDCD is
a new RGBD dataset of metallic industrial objects, collected with a depth
camera mounted on a robotic manipulator. The main purpose of this dataset is to
facilitate the training of domain-specific depth completion models, to be used
in logistics and manufacturing tasks. We trained a State-of-the-Art depth
completion model on this dataset, and report the results, setting an initial
benchmark. Further, we propose to use this dataset for learning
synthetic-to-depth-camera domain adaptation. Modifying synthetic RGBD data to
mimic characteristics of real-world depth acquisition could potentially enhance
training on synthetic data. For this end, we trained a Generative Adversarial
Network (GAN) on a synthetic industrial dataset and our real-world data.
Finally, to address geometric distortions in the generated images, we introduce
an auxiliary loss that promotes preservation of the original shape. The BIDCD
data is publicly available at https://zenodo.org/communities/bidcd.
