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.

翻译：在当前深度学习范式下，训练数据的数量和质量与网络架构及其训练细节同等关键。然而，大规模采集、处理和标注真实数据既困难又昂贵且耗时，尤其是对于3D物体配准等任务。尽管可以创建合成数据集，但这需要专业知识进行设计，且包含的类别数量有限。在本文中，我们提出了一种名为AutoSynth的新方法，该方法能够自动生成用于点云配准的3D训练数据。具体而言，AutoSynth通过探索一个包含数百万个潜在数据集（涵盖各种3D形状）的搜索空间，以低成本自动筛选出最优数据集。为实现这一目标，我们通过组装形状基元生成合成3D数据集，并开发了一种元学习策略来搜索最适合真实点云3D配准的训练数据。为确保搜索过程的可操作性，我们用一个小得多的替代网络替换了点云配准网络，从而实现了4056.43倍的加速。我们通过将所提方法应用于两种不同的点云配准网络（BPNet和IDAM）来证明其通用性。在TUD-L、LINEMOD和Occluded-LINEMOD数据集上的结果表明，使用我们搜索到的数据集训练神经网络，其性能始终优于使用广泛使用的ModelNet40数据集训练的相同网络。