3D-sensing is increasingly being used everywhere, including tablets, smartphones, robots, and autonomous vehicles. One major limitation to the usage and application of 3D-depth data is that very few databases have clean and accessible data, preventing researchers from building new applications and algorithms. This paper proposes 3D-ImageNet -- an analogue to the original ImageNet, which spurred the 2D image-processing AI explosion. 3D-ImageNet’s goal is to do the same for 3D-depth-sensing as was experienced for 2D images. This paper describes an open-source system that multiple cellphone users can use to collect and label a large amount of 3D data.