Taylor Series-Inspired Local Structure Fitting Network for Few-shot Point Cloud Semantic Segmentation
This addresses the problem of segmenting new categories in point clouds with limited data for applications like robotics and autonomous driving, offering an incremental advance over existing methods.
The paper tackles few-shot point cloud semantic segmentation by proposing a pretraining-free network, TaylorSeg, which uses a novel convolution inspired by Taylor series to fit local structures, achieving improvements of +2.28% and +4.37% mIoU on S3DIS and ScanNet datasets under a 2-way 1-shot setting.
Few-shot point cloud semantic segmentation aims to accurately segment "unseen" new categories in point cloud scenes using limited labeled data. However, pretraining-based methods not only introduce excessive time overhead but also overlook the local structure representation among irregular point clouds. To address these issues, we propose a pretraining-free local structure fitting network for few-shot point cloud semantic segmentation, named TaylorSeg. Specifically, inspired by Taylor series, we treat the local structure representation of irregular point clouds as a polynomial fitting problem and propose a novel local structure fitting convolution, called TaylorConv. This convolution learns the low-order basic information and high-order refined information of point clouds from explicit encoding of local geometric structures. Then, using TaylorConv as the basic component, we construct two variants of TaylorSeg: a non-parametric TaylorSeg-NN and a parametric TaylorSeg-PN. The former can achieve performance comparable to existing parametric models without pretraining. For the latter, we equip it with an Adaptive Push-Pull (APP) module to mitigate the feature distribution differences between the query set and the support set. Extensive experiments validate the effectiveness of the proposed method. Notably, under the 2-way 1-shot setting, TaylorSeg-PN achieves improvements of +2.28% and +4.37% mIoU on the S3DIS and ScanNet datasets respectively, compared to the previous state-of-the-art methods. Our code is available at https://github.com/changshuowang/TaylorSeg.