  In the current deep learning paradigm, the amount and quality of training
data are as critical as the network architecture and its training details.
However, collecting, processing, and annotating real data at scale is
difficult, expensive, and time-consuming, particularly for tasks such as 3D
object registration. While synthetic datasets can be created, they require
expertise to design and include a limited number of categories. In this paper,
we introduce a new approach called AutoSynth, which automatically generates 3D
training data for point cloud registration. Specifically, AutoSynth
automatically curates an optimal dataset by exploring a search space
encompassing millions of potential datasets with diverse 3D shapes at a low
cost.To achieve this, we generate synthetic 3D datasets by assembling shape
primitives, and develop a meta-learning strategy to search for the best
training data for 3D registration on real point clouds. For this search to
remain tractable, we replace the point cloud registration network with a much
smaller surrogate network, leading to a $4056.43$ times speedup. We demonstrate
the generality of our approach by implementing it with two different point
cloud registration networks, BPNet and IDAM. Our results on TUD-L, LINEMOD and
Occluded-LINEMOD evidence that a neural network trained on our searched dataset
yields consistently better performance than the same one trained on the widely
used ModelNet40 dataset.
