PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation

Abstract

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.

SHRED: 3D Shape Region Decomposition with Learned Local Operations

“…Without annotations, these approaches rely on global reconstruction-based losses, resulting in decompositions that well-represent coarse structures, but often ignore fine-grained regions of interest. Relatedly, some methods use primitive decompositions to formulate self-supervised losses that augment training to improve few-shot semantic labeling [Gadelha et al 2020;Sharma et al 2021].…”