We present SURFIT, a simple approach for label efficient learning of 3D shape segmentation networks. SURFIT is based on a self-supervised task of decomposing the surface of a 3D shape into geometric primitives. It can be readily applied to existing network architectures for 3D shape segmentation, and improves their performance in the few-shot setting, as we demonstrate in the widely used ShapeNet and PartNet benchmarks. SURFIT outperforms the prior stateof-the-art in this setting, suggesting that decomposability into primitives is a useful prior for learning representations predictive of semantic parts. We present a number of experiments varying the choice of geometric primitives and downstream tasks to demonstrate the effectiveness of the method.

The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. But both of these problems suffer from relatively small training sets, creating the need for statistically efficient methods to learn 3D shape representations. In this work, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations. Decomposing a 3D shape into simpler constituent parts or primitives is a fundamental problem in geometrical shape processing. There has been extensive work on such decompositions, where the criterion for simplicity of a constituent shape is often defined in terms of convexity for solid primitives. In this paper, we show that using the results of ACD to approximate a ground truth segmentation provides excellent self-supervision for learning 3D point cloud representations that are highly effective on downstream tasks. We report improvements over the state-of-theart in unsupervised representation learning on the ModelNet40 shape classification dataset and significant gains in few-shot part segmentation on the ShapeNetPart dataset. Code available at https://github.com/ matheusgadelha/PointCloudLearningACD . * equal contribution.