  Massive semantically labeled datasets are readily available for 2D images,
however, are much harder to achieve for 3D scenes. Objects in 3D repositories
like ShapeNet are labeled, but regrettably only in isolation, so without
context. 3D scenes can be acquired by range scanners on city-level scale, but
much fewer with semantic labels. Addressing this disparity, we introduce a new
optimization procedure, which allows training for 3D detection with raw 3D
scans while using as little as 5% of the object labels and still achieve
comparable performance. Our optimization uses two networks. A scene network
maps an entire 3D scene to a set of 3D object centers. As we assume the scene
not to be labeled by centers, no classic loss, such as Chamfer can be used to
train it. Instead, we use another network to emulate the loss. This loss
network is trained on a small labeled subset and maps a non centered 3D object
in the presence of distractions to its own center. This function is very
similar - and hence can be used instead of - the gradient the supervised loss
would provide. Our evaluation documents competitive fidelity at a much lower
level of supervision, respectively higher quality at comparable supervision.
Supplementary material can be found at: https://dgriffiths3.github.io.
